FDT-PC: Enhancing Adversarial Transferability through Frequency Domain Transformation and Perceptual Constraints

Abstract

Adversarial examples, crafted by introducing imperceptible perturbations to clean inputs, pose a serious threat to the robustness of deep learning models, particularly in image classification. While existing white-box attack methods often achieve high success rates, their effectiveness in black-box settings remains limited, especially when there is a gap between the source and target models. To address this challenge, we propose FDT-PC (Frequency Domain Transformation with Perceptual Constraints), a novel method that enhances adversarial transferability across different model architectures. Our approach transforms input images into the frequency domain using the Fourier transform, allowing richer semantic information to be captured—especially beneficial for Transformer-based models that leverage global frequency patterns. We then apply frequency-aware scaling and masking to decouple adversarial perturbations from model-specific features, thereby improving cross-model attack success. Furthermore, we incorporate a perceptual loss computed via a pre-trained VGG network to minimize high-level feature discrepancies, maintaining visual similarity while enhancing transferability. Experiments on the ImageNet dataset demonstrate that FDT-PC achieves superior black-box attack performance on both CNNs and Vision Transformers, outperforming existing state-of-the-art input transformation methods. These results highlight the effectiveness of frequency-domain perturbations combined with perceptual constraints in improving adversarial robustness evaluation.