Full updated code with finding ai generated images too

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoints/model.pth filter=lfs diff=lfs merge=lfs -text
+checkpoints/efficientnet.onnx filter=lfs diff=lfs merge=lfs -textvideos/0317.mp4 filter=lfs diff=lfs merge=lfs -text
+videos/celeb_synthesis.mp4 filter=lfs diff=lfs merge=lfs -text
+images/lady.png filter=lfs diff=lfs merge=lfs -text
+*.ext filter=lfs diff=lfs merge=lfs -text

.gitignore

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+checkpoints/RawNet2.pth
+deepfake/
+.gradio/

README.md

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+# DeepSecure-AI
+
+DeepSecure-AI is a powerful open-source tool designed to detect fake images, videos, and audios. Utilizing state-of-the-art deep learning techniques like EfficientNetV2 and MTCNN, DeepSecure-AI offers frame-by-frame video analysis, enabling high-accuracy deepfake detection. It's developed with a focus on ease of use, making it accessible for researchers, developers, and security analysts...
+
+---
+
+## Features
+
+- Multimedia Detection: Detect deepfakes in images, videos, and audio files using a unified platform.
+- High Accuracy: Leverages EfficientNetV2 for enhanced prediction performance and accurate results.
+- Real-Time Video Analysis: Frame-by-frame analysis of videos with automatic face detection.
+- User-Friendly Interface: Easy-to-use interface built with Gradio for uploading and processing media files.
+- Open Source: Completely open source under the MIT license, making it available for developers to extend and improve.
+
+---
+
+## Demo-Data
+
+You can test the deepfake detection capabilities of DeepSecure-AI by uploading your video files. The tool will analyze each frame of the video, detect faces, and determine the likelihood of the video being real or fake.
+
+Examples:  
+1. [Video1-fake-1-ff.mp4](#)
+2. [Video6-real-1-ff.mp4](#)
+
+---
+
+## How It Works
+
+DeepSecure-AI uses the following architecture:
+
+1. Face Detection:  
+   The [MTCNN](https://arxiv.org/abs/1604.02878) model detects faces in each frame of the video. If no face is detected, it will use the previous frame's face to ensure accuracy.
+
+2. Fake vs. Real Classification:  
+   Once the face is detected, it's resized and fed into the [EfficientNetV2](https://arxiv.org/abs/2104.00298) deep learning model, which determines the likelihood of the frame being real or fake.
+
+3. Fake Confidence:  
+   A final prediction is generated as a percentage score, indicating the confidence that the media is fake.
+
+4. Results:  
+   DeepSecure-AI provides an output video, highlighting the detected faces and a summary of whether the input is classified as real or fake.
+
+---
+
+## Project Setup
+
+### Prerequisites
+
+Ensure you have the following installed:
+
+- Python 3.10
+- Gradio (pip install gradio)
+- TensorFlow (pip install tensorflow)
+- OpenCV (pip install opencv-python)
+- PyTorch (pip install torch torchvision torchaudio)
+- facenet-pytorch (pip install facenet-pytorch)
+- MoviePy (pip install moviepy)
+
+### Installation
+
+1. Clone the repository:
+        git clone https://github.com/Divith123/DeepSecure-AI.git
+    cd DeepSecure-AI
+    
+
+2. Install required dependencies:
+        pip install -r requirements.txt
+    
+
+3. Download the pre-trained model weights for EfficientNetV2 and place them in the project folder.
+
+### Running the Application
+
+1. Launch the Gradio interface:
+        python app.py
+    
+
+2. The web interface will be available locally. You can upload a video, and DeepSecure-AI will analyze and display results.
+
+---
+
+## Example Usage
+
+Upload a video or image to DeepSecure-AI to detect fake media. Here are some sample predictions:
+
+- Video Analysis: The tool will detect faces from each frame and classify whether the video is fake or real.
+- Result Output: A GIF or MP4 file with the sequence of detected faces and classification result will be provided.
+
+---
+
+## Technologies Used
+
+- TensorFlow: For building and training deep learning models.
+- EfficientNetV2: The core model for image and video classification.
+- MTCNN: For face detection in images and videos.
+- OpenCV: For video processing and frame manipulation.
+- MoviePy: For video editing and result generation.
+- Gradio: To create a user-friendly interface for interacting with the deepfake detector.
+
+---
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+---
+
+## Contributions
+
+Contributions are welcome! If you'd like to improve the tool, feel free to submit a pull request or raise an issue.
+
+For more information, check the [Contribution Guidelines](CONTRIBUTING.md).
+
+---
+
+## References
+- Li et al. (2020): [Celeb-DF(V2)](https://arxiv.org/abs/2008.06456)
+- Rossler et al. (2019): [FaceForensics++](https://arxiv.org/abs/1901.08971)
+- Timesler (2020): [Facial Recognition Model in PyTorch](https://www.kaggle.com/timesler/facial-recognition-model-in-pytorch)
+
+---
+
+### Disclaimer
+
+DeepSecure-AI is a research project and is designed for educational purposes.Please use responsibly and always give proper credit when utilizing the model in your work.

app.py

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+import gradio as gr
+import inference_2 as inference
+
+# Title and Description
+title = " Multimodal Deepfake Detector"
+description = "Detect deepfakes and AI-generated content from videos, audio, and images using advanced AI models."
+
+# Individual Interfaces
+video_interface = gr.Interface(
+    inference.deepfakes_video_predict,
+    inputs=gr.Video(label="Upload a Video"),
+    outputs=gr.Textbox(label="Prediction"),
+    examples=["videos/aaa.mp4", "videos/bbb.mp4"],
+    cache_examples=False
+)
+
+image_interface = gr.Interface(
+    inference.deepfakes_image_predict,
+    inputs=gr.Image(label="Upload an Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    examples=["images/lady.jpg", "images/fake_image.jpg"],
+    cache_examples=False
+)
+
+audio_interface = gr.Interface(
+    inference.deepfakes_spec_predict,
+    inputs=gr.Audio(label="Upload an Audio"),
+    outputs=gr.Textbox(label="Prediction"),
+    examples=["audios/DF_E_2000027.flac", "audios/DF_E_2000031.flac"],
+    cache_examples=False
+)
+
+ai_image_detector = gr.Interface(
+    fn=inference.detect_ai_generated_image,
+    inputs=gr.Image(label="Upload an Image"),
+    outputs=gr.Textbox(label="AI-Generated or Human-Created"),
+    examples=["images/ai_generated.jpg", "images/real.jpeg"],
+    cache_examples=False
+)
+
+# 🧩 Full UI with Title & Tabs
+with gr.Blocks(title=title) as app:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+
+    with gr.Tab("🎬 Video Inference"):
+        video_interface.render()
+
+    with gr.Tab("🎧 Audio Inference"):
+        audio_interface.render()
+
+    with gr.Tab("🖼️ Image Inference"):
+        image_interface.render()
+
+    with gr.Tab("🤖 AI Image Detector"):
+        ai_image_detector.render()
+
+if __name__ == '__main__':
+    app.launch(share=False)

audio.py

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+import os
+os.system("ffprobe -version")

checkpoints/efficientnet.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:206f99f4c4efe6d088ba6e53bfcdec76ffa796a345d50770c037005e3cd11639
+size 23510323

checkpoints/model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3de812710093068acee6200b8d162aab074975edffa3edf2ccbe562868e4adf6
+size 117418889

data/__init__.py

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+import torch.utils.data
+
+class DataProvider():
+
+    def __init__(self, cfg, dataset, batch_size=None, shuffle=True):
+        super().__init__()
+        self.dataset = dataset
+        if batch_size is None:
+            batch_size = cfg.BATCH_SIZE
+        self.dataloader = torch.utils.data.DataLoader(
+            self.dataset,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            num_workers=int(cfg.WORKERS),
+            drop_last=False)
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __iter__(self):
+        for i, data in enumerate(self.dataloader):
+            yield data

data/augmentation_utils.py

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+import cv2
+import librosa
+import numpy as np
+import albumentations
+from albumentations import (Compose, ImageCompression, GaussNoise, HorizontalFlip, 
+                            PadIfNeeded, OneOf,ToGray, ShiftScaleRotate, GaussianBlur,
+                            RandomBrightnessContrast, FancyPCA, HueSaturationValue, BasicTransform)
+
+
+class AudioTransform(BasicTransform):
+    """ Transform for audio task. This is the main class where we override the targets and update params function for our need"""
+    @property
+    def targets(self):
+        return {"data": self.apply}
+    
+    def update_params(self, params, **kwargs):
+        if hasattr(self, "interpolation"):
+            params["interpolation"] = self.interpolation
+        if hasattr(self, "fill_value"):
+            params["fill_value"] = self.fill_value
+        return params
+
+class TimeShifting(AudioTransform):
+    """ Do time shifting of audio """
+    def __init__(self, always_apply=False, p=0.5):
+        super(TimeShifting, self).__init__(always_apply, p)
+        
+    def apply(self,data,**params):
+        '''
+        data : ndarray of audio timeseries
+        '''        
+        start_ = int(np.random.uniform(-80000,80000))
+        if start_ >= 0:
+            audio_time_shift = np.r_[data[start_:], np.random.uniform(-0.001,0.001, start_)]
+        else:
+            audio_time_shift = np.r_[np.random.uniform(-0.001,0.001, -start_), data[:start_]]
+        
+        return audio_time_shift
+
+class PitchShift(AudioTransform):
+    """ Do time shifting of audio """
+    def __init__(self, always_apply=False, p=0.5 , n_steps=None):
+        super(PitchShift, self).__init__(always_apply, p)
+        '''
+        nsteps here is equal to number of semitones
+        '''
+        
+        self.n_steps = n_steps
+        
+    def apply(self,data,**params):
+        '''
+        data : ndarray of audio timeseries
+        '''        
+        return librosa.effects.pitch_shift(data,sr=16000,n_steps=self.n_steps)
+
+
+class AddGaussianNoise(AudioTransform):
+    """ Do time shifting of audio """
+    def __init__(self, always_apply=False, p=0.5):
+        super(AddGaussianNoise, self).__init__(always_apply, p)
+        
+        
+    def apply(self,data,**params):
+        '''
+        data : ndarray of audio timeseries
+        ''' 
+        noise = np.random.randn(len(data))
+        data_wn = data + 0.005*noise
+        return data_wn
+
+
+create_frame_transforms = Compose([
+        ImageCompression(quality_lower=60, quality_upper=100, p=0.5),
+        GaussNoise(p=0.1),
+        GaussianBlur(blur_limit=3, p=0.05),
+        HorizontalFlip(),
+        PadIfNeeded(min_height=256, min_width=256, border_mode=cv2.BORDER_CONSTANT),
+        OneOf([RandomBrightnessContrast(), FancyPCA(), HueSaturationValue()], p=0.7),
+        ToGray(p=0.2),
+        ShiftScaleRotate(shift_limit=0.1, scale_limit=0.2, rotate_limit=10, border_mode=cv2.BORDER_CONSTANT, p=0.5),])
+
+
+
+create_spec_transforms = albumentations.Compose([
+        TimeShifting(p=0.9),  # here not p=1.0 because your nets should get some difficulties
+        AddGaussianNoise(p=0.8),
+        PitchShift(p=0.5,n_steps=4)
+    ])

data/dfdt_dataset.py

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+'''Module for loading the fakeavceleb dataset from tfrecord format'''
+import numpy as np
+import tensorflow as tf
+from data.augmentation_utils import create_frame_transforms, create_spec_transforms
+
+FEATURE_DESCRIPTION = {
+    'video_path': tf.io.FixedLenFeature([], tf.string), 
+    'image/encoded': tf.io.FixedLenFeature([], tf.string),
+    'clip/label/index': tf.io.FixedLenFeature([], tf.int64),
+    'clip/label/text': tf.io.FixedLenFeature([], tf.string), 
+    'WAVEFORM/feature/floats': tf.io.FixedLenFeature([], tf.string)
+}
+
+@tf.function
+def _parse_function(example_proto):
+
+    #Parse the input `tf.train.Example` proto using the dictionary above.
+    example = tf.io.parse_single_example(example_proto, FEATURE_DESCRIPTION)
+    
+    video_path = example['video_path']
+    video = tf.io.decode_raw(example['image/encoded'], tf.int8)    
+    spectrogram = tf.io.decode_raw(example['WAVEFORM/feature/floats'], tf.float32)
+    
+    label = example["clip/label/text"]
+    label_map = example["clip/label/index"]
+    
+    return video, spectrogram, label_map
+
+@tf.function
+def decode_inputs(video, spectrogram, label_map):
+    '''Decode tensors to arrays with desired shape'''
+    frame = tf.reshape(video, [10, 3, 256, 256])
+    frame = frame[0] / 255 #Pick the first frame and normalize it.
+    # frame = tf.cast(frame, tf.float32)
+
+    label_map = tf.expand_dims(label_map, axis = 0)
+    
+    sample = {'video_reshaped': frame, 'spectrogram': spectrogram, 'label_map': label_map}
+    return sample
+
+
+def decode_train_inputs(video, spectrogram, label_map):
+    #Data augmentation for spectograms
+    spectrogram_shape = spectrogram.shape
+    spec_augmented = tf.py_function(aug_spec_fn, [spectrogram], tf.float32)
+    spec_augmented.set_shape(spectrogram_shape)
+
+    frame = tf.reshape(video, [10, 256, 256, 3])
+    frame = frame[0] #Pick the first frame.
+    frame = frame / 255 #Normalize tensor.
+
+    frame_augmented = tf.py_function(aug_img_fn, [frame], tf.uint8)
+    # frame_augmented.set_shape(frame_shape)
+
+    frame_augmented.set_shape([3, 256, 256])
+    label_map = tf.expand_dims(label_map, axis = 0)
+
+    augmented_sample = {'video_reshaped': frame_augmented, 'spectrogram': spec_augmented, 'label_map': label_map}
+    return augmented_sample
+
+
+def aug_img_fn(frame):
+  frame = frame.numpy().astype(np.uint8)
+  frame_data = {'image': frame}
+  aug_frame_data = create_frame_transforms(**frame_data)
+  aug_img = aug_frame_data['image']
+  aug_img = aug_img.transpose(2, 0, 1)
+  return aug_img
+
+def aug_spec_fn(spec):
+  spec = spec.numpy()
+  spec_data = {'spec': spec}
+  aug_spec_data = create_spec_transforms(**spec_data)
+  aug_spec = aug_spec_data['spec']
+  return aug_spec
+
+
+class FakeAVCelebDatasetTrain:
+
+    def __init__(self, args):
+        self.args = args
+        self.samples = self.load_features_from_tfrec()
+
+    def load_features_from_tfrec(self):
+        '''Loads raw features from a tfrecord file and returns them as raw inputs'''
+        ds = tf.io.matching_files(self.args.data_dir)
+        files = tf.random.shuffle(ds)
+
+        shards = tf.data.Dataset.from_tensor_slices(files)
+        dataset = shards.interleave(tf.data.TFRecordDataset)
+        dataset = dataset.shuffle(buffer_size=100)
+
+        dataset = dataset.map(_parse_function, num_parallel_calls = tf.data.AUTOTUNE)
+        dataset = dataset.map(decode_train_inputs, num_parallel_calls = tf.data.AUTOTUNE)
+        dataset = dataset.padded_batch(batch_size = self.args.batch_size)
+        return dataset
+    
+    
+    def __len__(self):
+        self.samples = self.load_features_from_tfrec(self.args.data_dir)
+        cnt = self.samples.reduce(np.int64(0), lambda x, _: x + 1)
+        cnt = cnt.numpy()
+        return cnt
+
+class FakeAVCelebDatasetVal:
+
+    def __init__(self, args):
+        self.args = args
+        self.samples = self.load_features_from_tfrec()
+
+    def load_features_from_tfrec(self):
+        '''Loads raw features from a tfrecord file and returns them as raw inputs'''
+        ds = tf.io.matching_files(self.args.data_dir)
+        files = tf.random.shuffle(ds)
+
+        shards = tf.data.Dataset.from_tensor_slices(files)
+        dataset = shards.interleave(tf.data.TFRecordDataset)
+        dataset = dataset.shuffle(buffer_size=100)
+
+        dataset = dataset.map(_parse_function, num_parallel_calls = tf.data.AUTOTUNE)
+        dataset = dataset.map(decode_inputs, num_parallel_calls = tf.data.AUTOTUNE)
+        dataset = dataset.padded_batch(batch_size = self.args.batch_size)
+        return dataset
+    
+    
+    def __len__(self):
+        self.samples = self.load_features_from_tfrec(self.args.data_dir)
+        cnt = self.samples.reduce(np.int64(0), lambda x, _: x + 1)
+        cnt = cnt.numpy()
+        return cnt

data/generate_dataset_to_tfrecord.py

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+#Code outsourced from https://github.com/deepmind/dmvr/tree/master and later modified.
+
+"""Python script to generate TFRecords of SequenceExample from raw videos."""
+
+import contextlib
+import math
+import os
+import cv2
+from typing import Dict, Optional, Sequence
+import moviepy.editor
+from absl import app
+from absl import flags
+import ffmpeg
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+import warnings
+warnings.filterwarnings('ignore')
+
+flags.DEFINE_string("csv_path", "fakeavceleb_1k.csv", "Input csv")
+flags.DEFINE_string("output_path", "fakeavceleb_tfrec", "Tfrecords output path.")
+flags.DEFINE_string("video_root_path", "./",
+                    "Root directory containing the raw videos.")
+flags.DEFINE_integer(
+    "num_shards", 4, "Number of shards to output, -1 means"
+    "it will automatically adapt to the sqrt(num_examples).")
+flags.DEFINE_bool("decode_audio", False, "Whether or not to decode the audio")
+flags.DEFINE_bool("shuffle_csv", False, "Whether or not to shuffle the csv.")
+FLAGS = flags.FLAGS
+
+
+_JPEG_HEADER = b"\xff\xd8"
+
+
+@contextlib.contextmanager
+def _close_on_exit(writers):
+  """Call close on all writers on exit."""
+  try:
+    yield writers
+  finally:
+    for writer in writers:
+      writer.close()
+
+
+def add_float_list(key: str, values: Sequence[float],
+                   sequence: tf.train.SequenceExample):
+  sequence.feature_lists.feature_list[key].feature.add(
+  ).float_list.value[:] = values
+
+
+def add_bytes_list(key: str, values: Sequence[bytes],
+                   sequence: tf.train.SequenceExample):
+  sequence.feature_lists.feature_list[key].feature.add().bytes_list.value[:] = values
+
+
+def add_int_list(key: str, values: Sequence[int],
+                 sequence: tf.train.SequenceExample):
+  sequence.feature_lists.feature_list[key].feature.add().int64_list.value[:] = values
+
+
+def set_context_int_list(key: str, value: Sequence[int],
+                         sequence: tf.train.SequenceExample):
+  sequence.context.feature[key].int64_list.value[:] = value
+
+
+def set_context_bytes(key: str, value: bytes,
+                      sequence: tf.train.SequenceExample):
+  sequence.context.feature[key].bytes_list.value[:] = (value,)
+
+def set_context_bytes_list(key: str, value: Sequence[bytes],
+                           sequence: tf.train.SequenceExample):
+   sequence.context.feature[key].bytes_list.value[:] = value
+
+
+def set_context_float(key: str, value: float,
+                      sequence: tf.train.SequenceExample):
+  sequence.context.feature[key].float_list.value[:] = (value,)
+
+
+def set_context_int(key: str, value: int, sequence: tf.train.SequenceExample):
+  sequence.context.feature[key].int64_list.value[:] = (value,)
+
+
+def extract_frames(video_path, fps = 10, min_resize = 256):
+    '''Load n number of frames from a video'''
+    v_cap = cv2.VideoCapture(video_path)
+    v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    
+    if fps is None:
+        sample = np.arange(0, v_len)
+    else:
+        sample = np.linspace(0, v_len - 1, fps).astype(int)
+
+    frames = []
+    for j in range(v_len):
+        success = v_cap.grab()
+        if j in sample:
+            success, frame = v_cap.retrieve()
+            if not success:
+                continue
+                
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = cv2.resize(frame, (min_resize, min_resize))
+            frames.append(frame)
+    
+    v_cap.release()
+    frame_np = np.stack(frames)
+    return frame_np.tobytes()
+
+def extract_audio(video_path: str,
+                  sampling_rate: int = 16_000):
+  """Extract raw mono audio float list from video_path with ffmpeg."""
+  video = moviepy.editor.VideoFileClip(video_path)
+  audio = video.audio.to_soundarray()
+  #Load first channel.
+  audio = audio[:, 0]
+  
+  return np.array(audio)
+
+#Each of the features can be coerced into a tf.train.Example-compatible type using one of the _bytes_feature, _float_feature and the _int64_feature.
+#You can then create a tf.train.Example message from these encoded features.
+
+def serialize_example(video_path: str, label_name: str, label_map: Optional[Dict[str, int]] = None):
+    # Initiate the sequence example.
+    seq_example = tf.train.SequenceExample()
+
+    imgs_encoded = extract_frames(video_path, fps = 10)
+
+    audio = extract_audio(video_path)
+
+    set_context_bytes(f'image/encoded', imgs_encoded, seq_example)
+    set_context_bytes("video_path", video_path.encode(), seq_example)
+    set_context_bytes("WAVEFORM/feature/floats", audio.tobytes(), seq_example)
+    set_context_int("clip/label/index", label_map[label_name], seq_example)
+    set_context_bytes("clip/label/text", label_name.encode(), seq_example)
+    return seq_example
+
+
+def main(argv):
+    del argv
+    # reads the input csv.
+    input_csv = pd.read_csv(FLAGS.csv_path)
+    if FLAGS.num_shards == -1:
+        num_shards = int(math.sqrt(len(input_csv)))
+    else:
+        num_shards = FLAGS.num_shards
+    # Set up the TFRecordWriters.
+    basename = os.path.splitext(os.path.basename(FLAGS.csv_path))[0]
+    shard_names = [
+      os.path.join(FLAGS.output_path, f"{basename}-{i:05d}-of-{num_shards:05d}")
+      for i in range(num_shards)
+    ]
+    writers = [tf.io.TFRecordWriter(shard_name) for shard_name in shard_names]
+
+    if "label" in input_csv:
+        unique_labels = list(set(input_csv["label"].values))
+        l_map = {unique_labels[i]: i for i in range(len(unique_labels))}
+    else:
+        l_map = None
+
+    if FLAGS.shuffle_csv:
+        input_csv = input_csv.sample(frac=1)
+    with _close_on_exit(writers) as writers:
+        row_count = 0
+        for row in input_csv.itertuples():
+           index = row[0]
+           v = row[1]
+           if os.name == 'posix':
+            v = v.str.replace('\\', '/')
+           l = row[2]
+           row_count += 1
+           print("Processing example %d of %d   (%d%%) \r" %(row_count, len(input_csv), row_count * 100 / len(input_csv)), end="")
+           seq_ex = serialize_example(video_path = v, label_name = l,label_map = l_map)
+           writers[index % len(writers)].write(seq_ex.SerializeToString())
+
+if __name__ == "__main__":
+  app.run(main)

datasets/fakeavceleb_100.csv

@@ -0,0 +1,101 @@
+video_path,label
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00076/00109.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00166/00010.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00173/00118.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00366/00118.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00391/00052.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00475/00099.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00476/00109.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00478/00206.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00518/00031.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00701/00092.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00761/00072.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00781/00092.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00830/00143.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00944/00135.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id00987/00160.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01036/00010.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01076/00005.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01170/00021.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01171/00053.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01179/00160.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01207/00320.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01236/00005.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01392/00167.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01452/00001.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01521/00109.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01528/00017.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01530/00002.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01544/00044.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01597/00005.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01598/00044.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01610/00090.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01637/00002.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01691/00045.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01717/00005.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01779/00010.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01835/00130.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01856/00006.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01920/00099.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01933/00028.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01972/00078.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id01995/00071.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02005/00052.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02040/00476.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02051/00015.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02268/00036.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02296/00019.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02316/00094.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02342/00191.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id02494/00050.mp4,real
+FakeAVCeleb/RealVideo-RealAudio/African/men/id04727/00007.mp4,real
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_10_id00476_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_10_id01076_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_10_id01179_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_10_id02005_wavtolip.mp4,fake
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+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_12_id00518_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_12_id00761_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_12_id00987_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_12_id01856_wavtolip.mp4,fake
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+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_2_id00166_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_2_id00701_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_2_id01236_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00076/00109_2_id01521_wavtolip.mp4,fake
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+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00166/00010_id01637_5VjcPZm8knM_faceswap_id00166_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00166/00010_id01637_5VjcPZm8knM_faceswap_id00761_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00166/00010_id01637_5VjcPZm8knM_faceswap_id01171_wavtolip.mp4,fake
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+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00366/00118_id00076_Isiq7cA-DNE_faceswap_id01779_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00366/00118_id00076_Isiq7cA-DNE_faceswap_id02316_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00366/00118_id00076_Isiq7cA-DNE_faceswap_id02342_wavtolip.mp4,fake
+FakeAVCeleb/FakeVideo-FakeAudio/African/men/id00366/00118_id00076_Isiq7cA-DNE_faceswap_id02494_wavtolip.mp4,fake

datasets/fakeavceleb_1k.csv

@@ -0,0 +1,1001 @@
+video_path,label
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00076\00109.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00166\00010.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00173\00118.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00366\00118.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00391\00052.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00475\00099.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00476\00109.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00478\00206.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00518\00031.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00701\00092.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00761\00072.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00781\00092.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00830\00143.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id00944\00135.mp4,real
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+FakeAVCeleb\RealVideo-RealAudio\African\men\id01076\00005.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id01170\00021.mp4,real
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+FakeAVCeleb\RealVideo-RealAudio\African\men\id01207\00320.mp4,real
+FakeAVCeleb\RealVideo-RealAudio\African\men\id01236\00005.mp4,real
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inference.py

@@ -0,0 +1,211 @@
+import os
+import cv2
+import torch
+import argparse
+import numpy as np
+import torch.nn as nn
+from models.TMC import ETMC
+from models import image
+
+#Set random seed for reproducibility.
+torch.manual_seed(42)
+
+
+# Define the audio_args dictionary
+audio_args = {
+    'nb_samp': 64600,
+    'first_conv': 1024,
+    'in_channels': 1,
+    'filts': [20, [20, 20], [20, 128], [128, 128]],
+    'blocks': [2, 4],
+    'nb_fc_node': 1024,
+    'gru_node': 1024,
+    'nb_gru_layer': 3,
+}
+
+
+def get_args(parser):
+    parser.add_argument("--batch_size", type=int, default=8)
+    parser.add_argument("--data_dir", type=str, default="datasets/train/fakeavceleb*")
+    parser.add_argument("--LOAD_SIZE", type=int, default=256)
+    parser.add_argument("--FINE_SIZE", type=int, default=224)
+    parser.add_argument("--dropout", type=float, default=0.2)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+    parser.add_argument("--hidden", nargs="*", type=int, default=[])
+    parser.add_argument("--hidden_sz", type=int, default=768)
+    parser.add_argument("--img_embed_pool_type", type=str, default="avg", choices=["max", "avg"])
+    parser.add_argument("--img_hidden_sz", type=int, default=1024)
+    parser.add_argument("--include_bn", type=int, default=True)
+    parser.add_argument("--lr", type=float, default=1e-4)
+    parser.add_argument("--lr_factor", type=float, default=0.3)
+    parser.add_argument("--lr_patience", type=int, default=10)
+    parser.add_argument("--max_epochs", type=int, default=500)
+    parser.add_argument("--n_workers", type=int, default=12)
+    parser.add_argument("--name", type=str, default="MMDF")
+    parser.add_argument("--num_image_embeds", type=int, default=1)
+    parser.add_argument("--patience", type=int, default=20)
+    parser.add_argument("--savedir", type=str, default="./savepath/")
+    parser.add_argument("--seed", type=int, default=1)
+    parser.add_argument("--n_classes", type=int, default=2)
+    parser.add_argument("--annealing_epoch", type=int, default=10)
+    parser.add_argument("--device", type=str, default='cpu')
+    parser.add_argument("--pretrained_image_encoder", type=bool, default = False)
+    parser.add_argument("--freeze_image_encoder", type=bool, default = False)
+    parser.add_argument("--pretrained_audio_encoder", type = bool, default=False)
+    parser.add_argument("--freeze_audio_encoder", type = bool, default = False)
+    parser.add_argument("--augment_dataset", type = bool, default = True)
+
+    for key, value in audio_args.items():
+        parser.add_argument(f"--{key}", type=type(value), default=value)
+
+def model_summary(args):
+    '''Prints the model summary.'''
+    model = ETMC(args)
+
+    for name, layer in model.named_modules():
+        print(name, layer)
+
+def load_multimodal_model(args):
+    '''Load multimodal model'''
+    model = ETMC(args)
+    ckpt = torch.load('checkpoints/model_best.pt', map_location = torch.device('cpu'))
+    model.load_state_dict(ckpt,strict = False)
+    model.eval()
+    return model
+
+def load_img_modality_model(args):
+    '''Loads image modality model.'''
+    rgb_encoder = image.ImageEncoder(args)
+    ckpt = torch.load('checkpoints/model_best.pt', map_location = torch.device('cpu'))
+    rgb_encoder.load_state_dict(ckpt,strict = False)
+    rgb_encoder.eval()
+    return rgb_encoder
+
+def load_spec_modality_model(args):
+    spec_encoder = image.RawNet(args)
+    ckpt = torch.load('checkpoints/model_best.pt', map_location = torch.device('cpu'))
+    spec_encoder.load_state_dict(ckpt,strict = False)
+    spec_encoder.eval()
+    return spec_encoder
+
+
+#Load models.
+parser = argparse.ArgumentParser(description="Train Models")
+get_args(parser)
+args, remaining_args = parser.parse_known_args()
+assert remaining_args == [], remaining_args
+
+multimodal = load_multimodal_model(args)
+spec_model = load_spec_modality_model(args)
+img_model = load_img_modality_model(args)
+
+
+def preprocess_img(face):
+    face = face / 255
+    face = cv2.resize(face, (256, 256))
+    face = face.transpose(2, 0, 1) #(W, H, C) -> (C, W, H)
+    face_pt = torch.unsqueeze(torch.Tensor(face), dim = 0) 
+    return face_pt
+
+def preprocess_audio(audio_file):
+    audio_pt = torch.unsqueeze(torch.Tensor(audio_file), dim = 0)
+    return audio_pt
+
+def deepfakes_spec_predict(input_audio):
+    x, _ = input_audio
+    audio = preprocess_audio(x)
+    spec_grads = spec_model.forward(audio)
+    multimodal_grads = multimodal.spec_depth[0].forward(spec_grads)
+
+    out = nn.Softmax()(multimodal_grads)
+    max = torch.argmax(out, dim = -1) #Index of the max value in the tensor.
+    max_value = out[max] #Actual value of the tensor.
+    max_value = np.argmax(out[max].detach().numpy())
+
+    if max_value > 0.5:
+        preds = round(100 - (max_value*100), 3)
+        text2 = f"The audio is REAL."
+
+    else:
+        preds = round(max_value*100, 3)
+        text2 = f"The audio is FAKE."
+
+    return text2
+
+def deepfakes_image_predict(input_image):
+    face = preprocess_img(input_image)
+
+    img_grads = img_model.forward(face)
+    multimodal_grads = multimodal.clf_rgb[0].forward(img_grads)
+
+    out = nn.Softmax()(multimodal_grads)
+    max = torch.argmax(out, dim=-1) #Index of the max value in the tensor.
+    max = max.cpu().detach().numpy()
+    max_value = out[max] #Actual value of the tensor.
+    max_value = np.argmax(out[max].detach().numpy())
+
+    if max_value > 0.5:
+        preds = round(100 - (max_value*100), 3)
+        text2 = f"The image is REAL."
+
+    else:
+        preds = round(max_value*100, 3)
+        text2 = f"The image is FAKE."
+
+    return text2
+
+
+def preprocess_video(input_video, n_frames = 5):
+    v_cap = cv2.VideoCapture(input_video)
+    v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # Pick 'n_frames' evenly spaced frames to sample
+    if n_frames is None:
+        sample = np.arange(0, v_len)
+    else:
+        sample = np.linspace(0, v_len - 1, n_frames).astype(int)
+
+    #Loop through frames.
+    frames = []
+    for j in range(v_len):
+        success = v_cap.grab()
+        if j in sample:
+            # Load frame
+            success, frame = v_cap.retrieve()
+            if not success:
+                continue
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = preprocess_img(frame)
+            frames.append(frame)
+    v_cap.release()
+    return frames
+
+
+def deepfakes_video_predict(input_video):
+    '''Perform inference on a video.'''
+    video_frames = preprocess_video(input_video)
+
+    real_grads = []
+    fake_grads = []
+
+    for face in video_frames:
+        img_grads = img_model.forward(face)
+        multimodal_grads = multimodal.clf_rgb[0].forward(img_grads)
+
+        out = nn.Softmax()(multimodal_grads)
+        real_grads.append(out.cpu().detach().numpy()[0])
+        print(f"Video out tensor shape is: {out.shape}, {out}")
+
+        fake_grads.append(out.cpu().detach().numpy()[0])
+
+    real_grads_mean = np.mean(real_grads)
+    fake_grads_mean = np.mean(fake_grads)
+
+    if real_grads_mean > fake_grads_mean:
+        res = round(real_grads_mean * 100, 3)
+        text = f"The video is REAL."
+    else:
+        res = round(100 - (real_grads_mean * 100), 3)
+        text = f"The video is FAKE."
+    return text
+

inference_2.py

@@ -0,0 +1,265 @@
+import os
+import cv2
+import onnx
+import torch
+import argparse
+import numpy as np
+import torch.nn as nn
+from models.TMC import ETMC
+from models import image
+
+from onnx2pytorch import ConvertModel
+
+onnx_model = onnx.load('checkpoints/efficientnet.onnx')
+pytorch_model = ConvertModel(onnx_model)
+
+#Set random seed for reproducibility.
+torch.manual_seed(42)
+
+
+# Define the audio_args dictionary
+audio_args = {
+    'nb_samp': 64600,
+    'first_conv': 1024,
+    'in_channels': 1,
+    'filts': [20, [20, 20], [20, 128], [128, 128]],
+    'blocks': [2, 4],
+    'nb_fc_node': 1024,
+    'gru_node': 1024,
+    'nb_gru_layer': 3,
+    'nb_classes': 2
+}
+
+import torch
+from torchvision import transforms
+from PIL import Image
+from timm import create_model
+import os
+import numpy as np
+
+# Constants
+MODEL_PATH = r"models\ai_detector\pytorch_model.pth"
+IMG_SIZE = 380
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+LABEL_MAPPING = {0: "AI-generated", 1: "Human-created"}
+
+# Load model from local file
+model = create_model('efficientnet_b4', pretrained=False, num_classes=2)
+state_dict = torch.load(MODEL_PATH, map_location=DEVICE)
+model.load_state_dict(state_dict)
+model.to(DEVICE).eval()
+
+# Define preprocessing transform
+transform = transforms.Compose([
+    transforms.Resize(IMG_SIZE + 20),
+    transforms.CenterCrop(IMG_SIZE),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+def detect_ai_generated_image(img):
+    # Handle file path or numpy input
+    if isinstance(img, str) and os.path.isfile(img):
+        img = Image.open(img).convert("RGB")
+    elif isinstance(img, np.ndarray):
+        img = Image.fromarray(img.astype('uint8'), 'RGB')
+    elif isinstance(img, Image.Image):
+        img = img.convert("RGB")
+    else:
+        raise ValueError("Invalid image input")
+
+    input_tensor = transform(img).unsqueeze(0).to(DEVICE)
+
+    with torch.no_grad():
+        output = model(input_tensor)
+        probs = torch.nn.functional.softmax(output, dim=1)
+        pred_class = probs.argmax().item()
+        confidence = probs[0, pred_class].item()
+
+    return f"{LABEL_MAPPING[pred_class]} (confidence: {confidence:.2%})"
+
+
+def get_args(parser):
+    parser.add_argument("--batch_size", type=int, default=8)
+    parser.add_argument("--data_dir", type=str, default="datasets/train/fakeavceleb*")
+    parser.add_argument("--LOAD_SIZE", type=int, default=256)
+    parser.add_argument("--FINE_SIZE", type=int, default=224)
+    parser.add_argument("--dropout", type=float, default=0.2)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+    parser.add_argument("--hidden", nargs="*", type=int, default=[])
+    parser.add_argument("--hidden_sz", type=int, default=768)
+    parser.add_argument("--img_embed_pool_type", type=str, default="avg", choices=["max", "avg"])
+    parser.add_argument("--img_hidden_sz", type=int, default=1024)
+    parser.add_argument("--include_bn", type=int, default=True)
+    parser.add_argument("--lr", type=float, default=1e-4)
+    parser.add_argument("--lr_factor", type=float, default=0.3)
+    parser.add_argument("--lr_patience", type=int, default=10)
+    parser.add_argument("--max_epochs", type=int, default=500)
+    parser.add_argument("--n_workers", type=int, default=12)
+    parser.add_argument("--name", type=str, default="MMDF")
+    parser.add_argument("--num_image_embeds", type=int, default=1)
+    parser.add_argument("--patience", type=int, default=20)
+    parser.add_argument("--savedir", type=str, default="./savepath/")
+    parser.add_argument("--seed", type=int, default=1)
+    parser.add_argument("--n_classes", type=int, default=2)
+    parser.add_argument("--annealing_epoch", type=int, default=10)
+    parser.add_argument("--device", type=str, default='cpu')
+    parser.add_argument("--pretrained_image_encoder", type=bool, default = False)
+    parser.add_argument("--freeze_image_encoder", type=bool, default = False)
+    parser.add_argument("--pretrained_audio_encoder", type = bool, default=False)
+    parser.add_argument("--freeze_audio_encoder", type = bool, default = False)
+    parser.add_argument("--augment_dataset", type = bool, default = True)
+
+    for key, value in audio_args.items():
+        parser.add_argument(f"--{key}", type=type(value), default=value)
+
+def model_summary(args):
+    '''Prints the model summary.'''
+    model = ETMC(args)
+
+    for name, layer in model.named_modules():
+        print(name, layer)
+
+def load_multimodal_model(args):
+    '''Load multimodal model'''
+    model = ETMC(args)
+    ckpt = torch.load('checkpoints/model.pth', map_location = torch.device('cpu'))
+    model.load_state_dict(ckpt, strict = True)
+    model.eval()
+    return model
+
+def load_img_modality_model(args):
+    '''Loads image modality model.'''
+    rgb_encoder = pytorch_model
+
+    ckpt = torch.load('checkpoints/model.pth', map_location = torch.device('cpu'))
+    rgb_encoder.load_state_dict(ckpt['rgb_encoder'], strict = True)
+    rgb_encoder.eval()
+    return rgb_encoder
+
+def load_spec_modality_model(args):
+    spec_encoder = image.RawNet(args)
+    ckpt = torch.load('checkpoints/model.pth', map_location = torch.device('cpu'))
+    spec_encoder.load_state_dict(ckpt['spec_encoder'], strict = True)
+    spec_encoder.eval()
+    return spec_encoder
+
+
+#Load models.
+parser = argparse.ArgumentParser(description="Inference models")
+get_args(parser)
+args, remaining_args = parser.parse_known_args()
+assert remaining_args == [], remaining_args
+
+spec_model = load_spec_modality_model(args)
+
+img_model = load_img_modality_model(args)
+
+
+def preprocess_img(face):
+    face = face / 255
+    face = cv2.resize(face, (256, 256))
+    # face = face.transpose(2, 0, 1) #(W, H, C) -> (C, W, H)
+    face_pt = torch.unsqueeze(torch.Tensor(face), dim = 0) 
+    return face_pt
+
+def preprocess_audio(audio_file):
+    audio_pt = torch.unsqueeze(torch.Tensor(audio_file), dim = 0)
+    return audio_pt
+
+def deepfakes_spec_predict(input_audio):
+    x, _ = input_audio
+    audio = preprocess_audio(x)
+    spec_grads = spec_model.forward(audio)
+    spec_grads_inv = np.exp(spec_grads.cpu().detach().numpy().squeeze())
+
+    # multimodal_grads = multimodal.spec_depth[0].forward(spec_grads)
+
+    # out = nn.Softmax()(multimodal_grads)
+    # max = torch.argmax(out, dim = -1) #Index of the max value in the tensor.
+    # max_value = out[max] #Actual value of the tensor.
+    max_value = np.argmax(spec_grads_inv)
+
+    if max_value > 0.5:
+        preds = round(100 - (max_value*100), 3)
+        text2 = f"The audio is REAL."
+
+    else:
+        preds = round(max_value*100, 3)
+        text2 = f"The audio is FAKE."
+
+    return text2
+
+def deepfakes_image_predict(input_image):
+    face = preprocess_img(input_image)
+    print(f"Face shape is: {face.shape}")
+    img_grads = img_model.forward(face)
+    img_grads = img_grads.cpu().detach().numpy()
+    img_grads_np = np.squeeze(img_grads)
+
+    if img_grads_np[0] > 0.5:
+        preds = round(img_grads_np[0] * 100, 3)
+        text2 = f"The image is REAL. \nConfidence score is: {preds}"
+
+    else:
+        preds = round(img_grads_np[1] * 100, 3)
+        text2 = f"The image is FAKE. \nConfidence score is: {preds}"
+
+    return text2
+
+
+def preprocess_video(input_video, n_frames = 3):
+    v_cap = cv2.VideoCapture(input_video)
+    v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # Pick 'n_frames' evenly spaced frames to sample
+    if n_frames is None:
+        sample = np.arange(0, v_len)
+    else:
+        sample = np.linspace(0, v_len - 1, n_frames).astype(int)
+
+    #Loop through frames.
+    frames = []
+    for j in range(v_len):
+        success = v_cap.grab()
+        if j in sample:
+            # Load frame
+            success, frame = v_cap.retrieve()
+            if not success:
+                continue
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = preprocess_img(frame)
+            frames.append(frame)
+    v_cap.release()
+    return frames
+
+
+def deepfakes_video_predict(input_video):
+    '''Perform inference on a video.'''
+    video_frames = preprocess_video(input_video)
+    real_faces_list = []
+    fake_faces_list = []
+
+    for face in video_frames:
+        # face = preprocess_img(face)
+
+        img_grads = img_model.forward(face)
+        img_grads = img_grads.cpu().detach().numpy()
+        img_grads_np = np.squeeze(img_grads)
+        real_faces_list.append(img_grads_np[0])
+        fake_faces_list.append(img_grads_np[1])
+
+    real_faces_mean = np.mean(real_faces_list)
+    fake_faces_mean = np.mean(fake_faces_list)
+
+    if real_faces_mean > 0.5:
+        preds = round(real_faces_mean * 100, 3)
+        text2 = f"The video is REAL. \nConfidence score is: {preds}%"
+
+    else:
+        preds = round(fake_faces_mean * 100, 3)
+        text2 = f"The video is FAKE. \nConfidence score is: {preds}%"
+
+    return text2
+

inference_3.py

@@ -0,0 +1,17 @@
+# inference_2.py
+
+from PIL import Image
+import numpy as np
+
+def detect_ai_generated_image(img):
+    # if img is a path, load as array
+    if isinstance(img, str):
+        img = np.array(Image.open(img).convert("RGB"))
+
+    # 🧠 PLACEHOLDER: fake logic
+    # Replace with actual AI detection logic or model
+    mean_pixel = img.mean()
+    if mean_pixel > 120:
+        return "Possibly AI-generated"
+    else:
+        return "Likely Real"

main.py

@@ -0,0 +1,247 @@
+import os
+import argparse
+from tqdm import tqdm
+import torch.nn as nn
+import tensorflow as tf
+import torch.optim as optim
+
+from models.TMC import ETMC, ce_loss
+import torchvision.transforms as transforms
+from data.dfdt_dataset import FakeAVCelebDatasetTrain, FakeAVCelebDatasetVal
+
+
+from utils.utils import *
+from utils.logger import create_logger
+from sklearn.metrics import accuracy_score
+from torch.utils.tensorboard import SummaryWriter
+
+# Define the audio_args dictionary
+audio_args = {
+    'nb_samp': 64600,
+    'first_conv': 1024,
+    'in_channels': 1,
+    'filts': [20, [20, 20], [20, 128], [128, 128]],
+    'blocks': [2, 4],
+    'nb_fc_node': 1024,
+    'gru_node': 1024,
+    'nb_gru_layer': 3,
+}
+
+
+def get_args(parser):
+    parser.add_argument("--batch_size", type=int, default=8)
+    parser.add_argument("--data_dir", type=str, default="datasets/train/fakeavceleb*")
+    parser.add_argument("--LOAD_SIZE", type=int, default=256)
+    parser.add_argument("--FINE_SIZE", type=int, default=224)
+    parser.add_argument("--dropout", type=float, default=0.2)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+    parser.add_argument("--hidden", nargs="*", type=int, default=[])
+    parser.add_argument("--hidden_sz", type=int, default=768)
+    parser.add_argument("--img_embed_pool_type", type=str, default="avg", choices=["max", "avg"])
+    parser.add_argument("--img_hidden_sz", type=int, default=1024)
+    parser.add_argument("--include_bn", type=int, default=True)
+    parser.add_argument("--lr", type=float, default=1e-4)
+    parser.add_argument("--lr_factor", type=float, default=0.3)
+    parser.add_argument("--lr_patience", type=int, default=10)
+    parser.add_argument("--max_epochs", type=int, default=500)
+    parser.add_argument("--n_workers", type=int, default=12)
+    parser.add_argument("--name", type=str, default="MMDF")
+    parser.add_argument("--num_image_embeds", type=int, default=1)
+    parser.add_argument("--patience", type=int, default=20)
+    parser.add_argument("--savedir", type=str, default="./savepath/")
+    parser.add_argument("--seed", type=int, default=1)
+    parser.add_argument("--n_classes", type=int, default=2)
+    parser.add_argument("--annealing_epoch", type=int, default=10)
+    parser.add_argument("--device", type=str, default='cpu')
+    parser.add_argument("--pretrained_image_encoder", type=bool, default = False)
+    parser.add_argument("--freeze_image_encoder", type=bool, default = True)
+    parser.add_argument("--pretrained_audio_encoder", type = bool, default=False)
+    parser.add_argument("--freeze_audio_encoder", type = bool, default = True)
+    parser.add_argument("--augment_dataset", type = bool, default = True)
+
+    for key, value in audio_args.items():
+        parser.add_argument(f"--{key}", type=type(value), default=value)
+
+def get_optimizer(model, args):
+    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-5)
+    return optimizer
+
+
+def get_scheduler(optimizer, args):
+    return optim.lr_scheduler.ReduceLROnPlateau(
+        optimizer, "max", patience=args.lr_patience, verbose=True, factor=args.lr_factor
+    )
+
+def model_forward(i_epoch, model, args, ce_loss, batch):
+    rgb, spec, tgt = batch['video_reshaped'], batch['spectrogram'], batch['label_map']
+    rgb_pt = torch.Tensor(rgb.numpy())
+    spec = spec.numpy()
+    spec_pt = torch.Tensor(spec)
+    tgt_pt = torch.Tensor(tgt.numpy())
+
+    if torch.cuda.is_available():
+        rgb_pt, spec_pt, tgt_pt = rgb_pt.cuda(), spec_pt.cuda(), tgt_pt.cuda()
+        
+    # depth_alpha, rgb_alpha, depth_rgb_alpha = model(rgb_pt, spec_pt)
+
+    # loss = ce_loss(tgt_pt, depth_alpha, args.n_classes, i_epoch, args.annealing_epoch) + \
+    #        ce_loss(tgt_pt, rgb_alpha, args.n_classes, i_epoch, args.annealing_epoch) + \
+    #        ce_loss(tgt_pt, depth_rgb_alpha, args.n_classes, i_epoch, args.annealing_epoch)
+    # return loss, depth_alpha, rgb_alpha, depth_rgb_alpha, tgt_pt
+
+    depth_alpha, rgb_alpha, pseudo_alpha, depth_rgb_alpha = model(rgb_pt, spec_pt)
+
+    loss = ce_loss(tgt_pt, depth_alpha, args.n_classes, i_epoch, args.annealing_epoch) + \
+           ce_loss(tgt_pt, rgb_alpha, args.n_classes, i_epoch, args.annealing_epoch) + \
+           ce_loss(tgt_pt, pseudo_alpha, args.n_classes, i_epoch, args.annealing_epoch) + \
+           ce_loss(tgt_pt, depth_rgb_alpha, args.n_classes, i_epoch, args.annealing_epoch)
+    return loss, depth_alpha, rgb_alpha, depth_rgb_alpha, tgt_pt
+
+
+
+def model_eval(i_epoch, data, model, args, criterion):
+    model.eval()
+    with torch.no_grad():
+        losses, depth_preds, rgb_preds, depthrgb_preds, tgts = [], [], [], [], []
+        for batch in tqdm(data):
+            loss, depth_alpha, rgb_alpha, depth_rgb_alpha, tgt = model_forward(i_epoch, model, args, criterion, batch)
+            losses.append(loss.item())
+
+            depth_pred = depth_alpha.argmax(dim=1).cpu().detach().numpy()
+            rgb_pred = rgb_alpha.argmax(dim=1).cpu().detach().numpy()
+            depth_rgb_pred = depth_rgb_alpha.argmax(dim=1).cpu().detach().numpy()
+
+            depth_preds.append(depth_pred)
+            rgb_preds.append(rgb_pred)
+            depthrgb_preds.append(depth_rgb_pred)
+            tgt = tgt.cpu().detach().numpy()
+            tgts.append(tgt)
+
+    metrics = {"loss": np.mean(losses)}
+    print(f"Mean loss is: {metrics['loss']}")
+
+    tgts = [l for sl in tgts for l in sl]
+    depth_preds = [l for sl in depth_preds for l in sl]
+    rgb_preds = [l for sl in rgb_preds for l in sl]
+    depthrgb_preds = [l for sl in depthrgb_preds for l in sl]
+    metrics["spec_acc"] = accuracy_score(tgts, depth_preds)
+    metrics["rgb_acc"] = accuracy_score(tgts, rgb_preds)
+    metrics["specrgb_acc"] = accuracy_score(tgts, depthrgb_preds)
+    return metrics
+
+def write_weight_histograms(writer, step, model):
+    for idx, item in enumerate(model.named_parameters()):
+        name = item[0]
+        weights = item[1].data
+        if weights.size(dim = 0) > 2:
+            try:
+                writer.add_histogram(name, weights, idx)
+            except ValueError as e:
+                continue
+
+writer = SummaryWriter()
+
+def train(args):
+    set_seed(args.seed)
+    args.savedir = os.path.join(args.savedir, args.name)
+    os.makedirs(args.savedir, exist_ok=True)
+
+    train_ds = FakeAVCelebDatasetTrain(args)
+    train_ds = train_ds.load_features_from_tfrec()
+
+    val_ds = FakeAVCelebDatasetVal(args)
+    val_ds = val_ds.load_features_from_tfrec()
+    
+    model = ETMC(args)
+    optimizer = get_optimizer(model, args)
+    scheduler = get_scheduler(optimizer, args)
+    logger = create_logger("%s/logfile.log" % args.savedir, args)
+    if torch.cuda.is_available():
+        model.cuda()
+
+    torch.save(args, os.path.join(args.savedir, "checkpoint.pt"))
+    start_epoch, global_step, n_no_improve, best_metric = 0, 0, 0, -np.inf
+
+    for i_epoch in range(start_epoch, args.max_epochs):
+        train_losses = []
+        model.train()
+        optimizer.zero_grad()
+
+        for index, batch in tqdm(enumerate(train_ds)):
+            loss, depth_out, rgb_out, depthrgb, tgt = model_forward(i_epoch, model, args, ce_loss, batch)
+            if args.gradient_accumulation_steps > 1:
+                 loss = loss / args.gradient_accumulation_steps
+
+            train_losses.append(loss.item())
+            loss.backward()
+            global_step += 1
+            if global_step % args.gradient_accumulation_steps == 0:
+                optimizer.step()
+                optimizer.zero_grad()
+
+        #Write weight histograms to Tensorboard.
+        write_weight_histograms(writer, i_epoch, model)
+
+        model.eval()
+        metrics = model_eval(
+            np.inf, val_ds, model, args, ce_loss
+        )
+        logger.info("Train Loss: {:.4f}".format(np.mean(train_losses)))
+        log_metrics("val", metrics, logger)
+        logger.info(
+            "{}: Loss: {:.5f} | spec_acc: {:.5f}, rgb_acc: {:.5f}, depth rgb acc: {:.5f}".format(
+                "val", metrics["loss"], metrics["spec_acc"], metrics["rgb_acc"], metrics["specrgb_acc"]
+            )
+        )
+        tuning_metric = metrics["specrgb_acc"]
+
+        scheduler.step(tuning_metric)
+        is_improvement = tuning_metric > best_metric
+        if is_improvement:
+            best_metric = tuning_metric
+            n_no_improve = 0
+        else:
+            n_no_improve += 1
+
+        save_checkpoint(
+            {
+                "epoch": i_epoch + 1,
+                "optimizer": optimizer.state_dict(),
+                "scheduler": scheduler.state_dict(),
+                "n_no_improve": n_no_improve,
+                "best_metric": best_metric,
+            },
+            is_improvement,
+            args.savedir,
+        )
+
+        if n_no_improve >= args.patience:
+            logger.info("No improvement. Breaking out of loop.")
+            break
+    writer.close()
+    # load_checkpoint(model, os.path.join(args.savedir, "model_best.pt"))
+    model.eval()
+    test_metrics = model_eval(
+        np.inf, val_ds, model, args, ce_loss
+    )
+    logger.info(
+        "{}: Loss: {:.5f} | spec_acc: {:.5f}, rgb_acc: {:.5f}, depth rgb acc: {:.5f}".format(
+            "Test", test_metrics["loss"], test_metrics["spec_acc"], test_metrics["rgb_acc"],
+            test_metrics["depthrgb_acc"]
+        )
+    )
+    log_metrics(f"Test", test_metrics, logger)
+
+
+def cli_main():
+    parser = argparse.ArgumentParser(description="Train Models")
+    get_args(parser)
+    args, remaining_args = parser.parse_known_args()
+    assert remaining_args == [], remaining_args
+    train(args)
+
+
+if __name__ == "__main__":
+    import warnings
+    warnings.filterwarnings("ignore")
+    cli_main()

model.py

@@ -0,0 +1,3 @@
+from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights
+
+model = efficientnet_v2_s(weights=EfficientNet_V2_S_Weights.IMAGENET1K_V1)

models/TMC.py

@@ -0,0 +1,156 @@
+import torch
+import torch.nn as nn
+from models import image
+import torch.nn.functional as F
+
+
+# loss function
+def KL(alpha, c):
+    if torch.cuda.is_available():
+        beta = torch.ones((1, c)).cuda()
+    else:
+        beta = torch.ones((1, c))
+    S_alpha = torch.sum(alpha, dim=1, keepdim=True)
+    S_beta = torch.sum(beta, dim=1, keepdim=True)
+    lnB = torch.lgamma(S_alpha) - torch.sum(torch.lgamma(alpha), dim=1, keepdim=True)
+    lnB_uni = torch.sum(torch.lgamma(beta), dim=1, keepdim=True) - torch.lgamma(S_beta)
+    dg0 = torch.digamma(S_alpha)
+    dg1 = torch.digamma(alpha)
+    kl = torch.sum((alpha - beta) * (dg1 - dg0), dim=1, keepdim=True) + lnB + lnB_uni
+    return kl
+
+def ce_loss(p, alpha, c, global_step, annealing_step):
+    S = torch.sum(alpha, dim=1, keepdim=True)
+    E = alpha - 1
+    label = p
+    A = torch.sum(label * (torch.digamma(S) - torch.digamma(alpha)), dim=1, keepdim=True)
+
+    annealing_coef = min(1, global_step / annealing_step)
+    alp = E * (1 - label) + 1
+    B = annealing_coef * KL(alp, c)
+    return torch.mean((A + B))
+
+
+class TMC(nn.Module):
+    def __init__(self, args):
+        super(TMC, self).__init__()
+        self.args = args
+        self.rgbenc = image.ImageEncoder(args)
+        self.specenc = image.RawNet(args)
+        
+        spec_last_size = args.img_hidden_sz * 1
+        rgb_last_size = args.img_hidden_sz * args.num_image_embeds
+        self.spec_depth = nn.ModuleList()
+        self.clf_rgb = nn.ModuleList()
+
+        for hidden in args.hidden:
+            self.spec_depth.append(nn.Linear(spec_last_size, hidden))
+            self.spec_depth.append(nn.ReLU())
+            self.spec_depth.append(nn.Dropout(args.dropout))
+            spec_last_size = hidden
+        self.spec_depth.append(nn.Linear(spec_last_size, args.n_classes))
+
+        for hidden in args.hidden:
+            self.clf_rgb.append(nn.Linear(rgb_last_size, hidden))
+            self.clf_rgb.append(nn.ReLU())
+            self.clf_rgb.append(nn.Dropout(args.dropout))
+            rgb_last_size = hidden
+        self.clf_rgb.append(nn.Linear(rgb_last_size, args.n_classes))
+
+    def DS_Combin_two(self, alpha1, alpha2):
+        # Calculate the merger of two DS evidences
+        alpha = dict()
+        alpha[0], alpha[1] = alpha1, alpha2
+        b, S, E, u = dict(), dict(), dict(), dict()
+        for v in range(2):
+            S[v] = torch.sum(alpha[v], dim=1, keepdim=True)
+            E[v] = alpha[v] - 1
+            b[v] = E[v] / (S[v].expand(E[v].shape))
+            u[v] = self.args.n_classes / S[v]
+
+        # b^0 @ b^(0+1)
+        bb = torch.bmm(b[0].view(-1, self.args.n_classes, 1), b[1].view(-1, 1, self.args.n_classes))
+        # b^0 * u^1
+        uv1_expand = u[1].expand(b[0].shape)
+        bu = torch.mul(b[0], uv1_expand)
+        # b^1 * u^0
+        uv_expand = u[0].expand(b[0].shape)
+        ub = torch.mul(b[1], uv_expand)
+        # calculate K
+        bb_sum = torch.sum(bb, dim=(1, 2), out=None)
+        bb_diag = torch.diagonal(bb, dim1=-2, dim2=-1).sum(-1)
+        # bb_diag1 = torch.diag(torch.mm(b[v], torch.transpose(b[v+1], 0, 1)))
+        K = bb_sum - bb_diag
+
+        # calculate b^a
+        b_a = (torch.mul(b[0], b[1]) + bu + ub) / ((1 - K).view(-1, 1).expand(b[0].shape))
+        # calculate u^a
+        u_a = torch.mul(u[0], u[1]) / ((1 - K).view(-1, 1).expand(u[0].shape))
+        # test = torch.sum(b_a, dim = 1, keepdim = True) + u_a #Verify programming errors
+
+        # calculate new S
+        S_a = self.args.n_classes / u_a
+        # calculate new e_k
+        e_a = torch.mul(b_a, S_a.expand(b_a.shape))
+        alpha_a = e_a + 1
+        return alpha_a
+
+    def forward(self, rgb, spec):
+        spec = self.specenc(spec)
+        spec = torch.flatten(spec, start_dim=1)
+
+        rgb = self.rgbenc(rgb)
+        rgb = torch.flatten(rgb, start_dim=1)
+
+        spec_out = spec
+
+        for layer in self.spec_depth:
+            spec_out = layer(spec_out)
+
+        rgb_out = rgb
+
+        for layer in self.clf_rgb:
+            rgb_out = layer(rgb_out)
+
+        spec_evidence, rgb_evidence = F.softplus(spec_out), F.softplus(rgb_out)
+        spec_alpha, rgb_alpha = spec_evidence+1, rgb_evidence+1
+        spec_rgb_alpha = self.DS_Combin_two(spec_alpha, rgb_alpha)
+        return spec_alpha, rgb_alpha, spec_rgb_alpha
+
+
+class ETMC(TMC):
+    def __init__(self, args):
+        super(ETMC, self).__init__(args)
+        last_size = args.img_hidden_sz * args.num_image_embeds + args.img_hidden_sz * args.num_image_embeds
+        self.clf = nn.ModuleList()
+        for hidden in args.hidden:
+            self.clf.append(nn.Linear(last_size, hidden))
+            self.clf.append(nn.ReLU())
+            self.clf.append(nn.Dropout(args.dropout))
+            last_size = hidden
+        self.clf.append(nn.Linear(last_size, args.n_classes))
+
+    def forward(self, rgb, spec):
+        spec = self.specenc(spec)
+        spec = torch.flatten(spec, start_dim=1)
+
+        rgb = self.rgbenc(rgb)
+        rgb = torch.flatten(rgb, start_dim=1)
+
+        spec_out = spec
+        for layer in self.spec_depth:
+            spec_out = layer(spec_out)
+
+        rgb_out = rgb
+        for layer in self.clf_rgb:
+            rgb_out = layer(rgb_out)
+
+        pseudo_out = torch.cat([rgb, spec], -1)
+        for layer in self.clf:
+            pseudo_out = layer(pseudo_out)
+
+        depth_evidence, rgb_evidence, pseudo_evidence = F.softplus(spec_out), F.softplus(rgb_out), F.softplus(pseudo_out)
+        depth_alpha, rgb_alpha, pseudo_alpha = depth_evidence+1, rgb_evidence+1, pseudo_evidence+1
+        depth_rgb_alpha = self.DS_Combin_two(self.DS_Combin_two(depth_alpha, rgb_alpha), pseudo_alpha)
+        return depth_alpha, rgb_alpha, pseudo_alpha, depth_rgb_alpha
+