Title: Adversarial Attack Detection Under Realistic Constraints. Abstract: While adversarial attacks are a serious threat for neural networks safety, existing defense mechanisms remain very limited regarding their applicability to real-world settings. Any industrial-driven attack detector is expected to meet three unavoidable requirements: (R1) being adapted to black-box scenario where the user has only access to the predicted probabilities, (R2) making fast inference and (R3) not involving any training phase. In this paper, we introduce REFEREE, the first detector that meets all these requirements while improving state-of-the-art performances. It leverages the concept of information projections (I-projection), which generalizes ideas coming from out-of-distribution detection and allows to extract relevant information contained in the softmax outputs of a network. Our extensive experiments demonstrates that REFEREE improves upon existing methods while considerably reducing the inference time: it requires less than 0.05 seconds by test input, which is up to 400 times faster than former methods. This makes REFEREE an excellent candidate for adversarial attacks detection in real-world applications.