Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
This work addresses security vulnerabilities in machine learning and vision systems by making attacks harder to detect, though it is incremental as it builds on existing image-scaling attack techniques.
The paper tackles the problem of visible artifacts in backdoor and poisoning attacks on neural networks by hiding triggers using image-scaling attacks, demonstrating that these combined attacks remain effective and evade current detection defenses.
Backdoors and poisoning attacks are a major threat to the security of machine-learning and vision systems. Often, however, these attacks leave visible artifacts in the images that can be visually detected and weaken the efficacy of the attacks. In this paper, we propose a novel strategy for hiding backdoor and poisoning attacks. Our approach builds on a recent class of attacks against image scaling. These attacks enable manipulating images such that they change their content when scaled to a specific resolution. By combining poisoning and image-scaling attacks, we can conceal the trigger of backdoors as well as hide the overlays of clean-label poisoning. Furthermore, we consider the detection of image-scaling attacks and derive an adaptive attack. In an empirical evaluation, we demonstrate the effectiveness of our strategy. First, we show that backdoors and poisoning work equally well when combined with image-scaling attacks. Second, we demonstrate that current detection defenses against image-scaling attacks are insufficient to uncover our manipulations. Overall, our work provides a novel means for hiding traces of manipulations, being applicable to different poisoning approaches.