Enhancing Adversarial Transferability With Alignment Network

Deep neural networks (DNNs) have been confirmed to exhibit vulnerability, as they are susceptible to deception by adversarial examples. Transfer-based attacks perturb a surrogate model and use the transferability of adversarial examples to attack other models. The effectiveness of these attacks relies heavily on the surrogate model, which often focuses on non-critical regions like backgrounds or object edges, leading to poor transferability. The intrinsic properties of the surrogate model fundamentally determine the performance of transfer-based attacks, yet this aspect has rarely been the focus of research. Therefore, we respectively design image masking operations for Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), forcing the model to reallocate attention to the critical regions. The attention of the surrogate model on the masked image and the original image is then aligned by inserting an alignment network inside the model. The modified surrogate model becomes more proficient in capturing the critical regions within the image, thereby generating more powerful adversarial examples. The proposed alignment network can be integrated into existing transfer-based attacks, significantly enhancing their performance. In addition, we also propose a novel feature-level attack based on the aligned attention, demonstrating superior performance compared to existing state-of-the-art feature-level attacks.