Generating Imperceptible and Cross-Resolution Remote Sensing Adversarial Examples Based on Implicit Neural Representations

Deep neural networks (DNNs) have been widely applied in remote sensing, and the research on its adversarial attack algorithm is the key to evaluating its robustness. Current adversarial attack methods primarily prioritize maximizing the attack success rate, disregarding the imperceptibility of the generated adversarial noise to human visual perception. Moreover, research on adversarial sample transferability has mostly focused on cross-model and cross-dataset scenarios, overlooking the investigation of adversarial attacks across different resolutions, while the rarely studied cross-resolution adversarial attacks are critical for remote sensing with different resolutions. In this article, we propose a novel method for generating imperceptible adversarial samples for cross-resolution remote sensing images based on implicit neural representations (INRs). By mapping the discrete images to a continuous neural functional space, we explicitly guarantee the visual quality of adversarial samples and decouple the model input from the image resolution. To enhance the visual fidelity of the generated adversarial samples, a multiscale discriminative learning scheme is proposed for the optimization process. For cross-resolution adversarial attacks, we align with images of different resolutions and generate cross-resolution adversarial perturbation by benefiting from the natural properties of the continuous resolution of INRs. To validate the effectiveness of our method, we compare it with the existing adversarial attacking methods using four evaluation metrics. Experiments show that our method achieves the best results in terms of attack success rate, imperceptibility, and cross-resolution attack transferability. Our code will be made publicly available.