STEAM: Style Transfer Enabled Adversarial Attack with Attention Mechanism on Remote Sensing Image Scene Classification

Research on adversarial attacks in remote sensing tasks have predominantly focused on designing perturbations or patches, presenting challenges in balancing attack success rate and adversarial stealthiness. Instead of focusing on designing adversarial examples under adversarial perturbation constraints to ensure stealthiness, this paper proposes a Style Transfer Enabled Adversarial Attack with Attention Mechanism (STEAM), which leverages style transfer to generate adversarial examples with high visual fidelity. Specifically, STEAM transfers distinctive styles from critical regions of source samples to attackable areas in target samples, effectively incorporating natural textures from source samples. To further refine this process, an attention mechanism is introduced to selectively extract style features from key regions of the source samples, mitigating redundancy from global style information. Additionally, selective style transfer process also includes the consideration of semantic features across different regions in target samples, ensuring a more effective attack area selection. As a result, STEAM achieves high attack success rate by utilizing proper style selected from groups of style samples, and preserving high visual fidelity through selectively transfer the natural style feature into specific attackable region in target samples. Experimental results on the UCM and WHU-RS19 datasets demonstrate that STEAM not only enhances the visual fidelity of adversarial examples but also improves the attack success rate. Furthermore, experiments against state-of-the-art adversarial defense methods highlight the adversarial attack effectiveness and robostness of STEAM compared to other adversarial attack methods.