Previous studies to defense against adversarial examples mostly focused on refining the DNN models but have either shown limited success or required expensive computation. In this paper, we introduce a new detection method against adversarial attacks. Since L0 attackers have similar search patterns, to separate clean examples from adversarial examples, we found a new distance measure on output layer. These strategies have low time and computing costs and can be easily complementary to other defenses. Moreover, our method performs well on adversarial noise localization task.
Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.
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