Evaluating Transferability of Attacks across Generative Models

The need for adversarial sample transferability is to attack black-box deep learning models. Whereas much recent work focuses on making untargeted adversarial attacks more transferable, there has been scarce research on the creation of transferable targeted adversarial instances that can trick models into believing they are of a particular class. The present transferable targeted adversarial attacks are not transferable since they cannot sufficiently define the distribution of target classes. In this paper, we propose a generative adversarial training system consisting of a feature-label dual discriminator to identify the adversarial instances formed from the target class images and a generator to construct targeted adversarial examples. It is concluded that adversarial scenarios have significant real-world applications in safety-critical fields like biometrics and autonomous driving. In addition, it is demonstrated that the current networks' susceptibility to hostile attacks, even under the worst black-box conditions has far-reaching societal consequences. We intend to further encourage more research into the inner workings of neural networks in the face of adversarial attacks, whereby people might use this knowledge to build robust defense mechanisms.