{"action":"created","comment":{"url":"https://api.github.com/repos/vitalab/vitalab.github.io/pulls/comments/239925795","pull_request_review_id":182846703,"id":239925795,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIzOTkyNTc5NQ==","diff_hunk":"@@ -0,0 +1,45 @@\n+---\n+layout: review\n+title: \"Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects\"\n+tags: deep-learning adversarial 3D CNN classification\n+author: \"Antoine Théberge\"\n+cite:\n+    authors: \"Michael A. Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, Anh Nguyen\"\n+    title:   \"Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects\"\n+    venue:   \"Arxiv\"\n+pdf: \"https://arxiv.org/abs/1811.10830\"\n+---\n+\n+## Introduction\n+\n+![](/deep-learning/images/strike_with_a_pose/summary.png)\n+\n+While convolutional neural networks (CNNs) have become increasingly good at object classification, adversarial attacks have come to challenge this expertise. While most adversarial attack research has been done by modifying pixels to fool classifiers, the authors suggest that CNNs can be fooled by Out of Distribution (OoD) poses of otherwise easily classified objects.  \n+\n+## Out of Distribution poses\n+\n+To formalize the problem of finding poses, the authors try to optimize the following problem:\n+\n+![](/deep-learning/images/strike_with_a_pose/argmax.png)\n+\n+With $$w$$ being the set of parameters for a 3D rotation and translation, $$f$$ being the classifier, $$t$$ the incorrect class, $$R$$ the rendered image and $$\\phi$$ the image parameters such as lighting, background image, vertices, texture, etc. They calculated the partial derivatives of $$w$$ and performed vanilla gradient-descent to minimize the cross-entropy loss $$L$$ w.r.t. a certain class\n+\n+![](/deep-learning/images/strike_with_a_pose/cross-entropy.png)\n+\n+![](/deep-learning/images/strike_with_a_pose/vgd.png)\n+\n+To backpropagate gradients to scene parameters, they both used a differentiable 3D renderer<sup>[1](https://github.com/hiroharu-kato/neural_renderer)</sup> and calculated gradient analytically, which was possible due to their small number of parameters (6). They also used random search (RS) to find parameters.","path":"deep-learning/_posts/2018-12-06-strike_with_a_pose.md","position":31,"original_position":31,"commit_id":"6982850151baf9783309789f1f9d69e9a68a4abf","original_commit_id":"6982850151baf9783309789f1f9d69e9a68a4abf","user":{"login":"lemairecarl","id":13444373,"node_id":"MDQ6VXNlcjEzNDQ0Mzcz","avatar_url":"https://avatars3.githubusercontent.com/u/13444373?v=4","gravatar_id":"","url":"https://api.github.com/users/lemairecarl","html_url":"https://github.com/lemairecarl","followers_url":"https://api.github.com/users/lemairecarl/followers","following_url":"https://api.github.com/users/lemairecarl/following{/other_user}","gists_url":"https://api.github.com/users/lemairecarl/gists{/gist_id}","starred_url":"https://api.github.com/users/lemairecarl/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lemairecarl/subscriptions","organizations_url":"https://api.github.com/users/lemairecarl/orgs","repos_url":"https://api.github.com/users/lemairecarl/repos","events_url":"https://api.github.com/users/lemairecarl/events{/privacy}","received_events_url":"https://api.github.com/users/lemairecarl/received_events","type":"User","site_admin":false},"body":"Since this is not a foot note, you should just make \"differentiable 3D renderer\" a link.","created_at":"2018-12-07T19:44:46Z","updated_at":"2018-12-07T19:47:36Z","html_url":"https://github.com/vitalab/vitalab.github.io/pull/294#discussion_r239925795","pull_request_url":"https://api.github.com/repos/vitalab/vitalab.github.io/pulls/294","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/vitalab/vitalab.github.io/pulls/comments/239925795"},"html":{"href":"https://github.com/vitalab/vitalab.github.io/pull/294#discussion_r239925795"},"pull_request":{"href":"https://api.github.com/repos/vitalab/vitalab.github.io/pulls/294"}}},"pull_request":{"url":"https://api.github.com/repos/vitalab/vitalab.github.io/pulls/294","id":236696554,"node_id":"MDExOlB1bGxSZXF1ZXN0MjM2Njk2NTU0","html_url":"https://github.com/vitalab/vitalab.github.io/pull/294","diff_url":"https://github.com/vitalab/vitalab.github.io/pull/294.diff","patch_url":"https://github.com/vitalab/vitalab.github.io/pull/294.patch","issue_url":"https://api.github.com/repos/vitalab/vitalab.github.io/issues/294","number":294,"state":"open","locked":false,"title":"Strike with a pose","user":{"login":"AntoineTheb","id":10272382,"node_id":"MDQ6VXNlcjEwMjcyMzgy","avatar_url":"https://avatars2.githubusercontent.com/u/10272382?v=4","gravatar_id":"","url":"https://api.github.com/users/AntoineTheb","html_url":"https://github.com/AntoineTheb","followers_url":"https://api.github.com/users/AntoineTheb/followers","following_url":"https://api.github.com/users/AntoineTheb/following{/other_user}","gists_url":"https://api.github.com/users/AntoineTheb/gists{/gist_id}","starred_url":"https://api.github.com/users/AntoineTheb/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/AntoineTheb/subscriptions","organizations_url":"https://api.github.com/users/AntoineTheb/orgs","repos_url":"https://api.github.com/users/AntoineTheb/repos","events_url":"https://api.github.com/users/AntoineTheb/events{/privacy}","received_events_url":"https://api.github.com/users/AntoineTheb/received_events","type":"User","site_admin":false},"body":"There's a comedic effect in seeing familiar objects in unfamiliar poses. Maybe that's what's throwing the classifiers 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