Segment Shards: Cross-Prompt Adversarial Attacks against the Segment Anything Model - Shize Huang; Qianhui Fan; Zhaoxin Zhang; Xiaowen Liu; Guanqun Song; Jinzhe Qin (Applied Sciences, 2024)
Foundation models play an increasingly pivotal role in the field of deep neural networks. Given that deep neural networks are widely used in real-world systems and are generally susceptible to adversarial attacks, securing foundation models becomes a key research issue. However, research on adversarial attacks against the Segment Anything Model (SAM), a visual foundation model, is still in its infancy. In this paper, we propose the prompt batch attack (PBA), which can effectively attack SAM, making it unable to capture valid objects or even generate fake shards. Extensive experiments were conducted to compare the adversarial attack performance among optimizing without prompts, optimizing all prompts, and optimizing batches of prompts as in PBA. Numerical results on multiple datasets show that the cross-prompt attack success rate (<semantics>ASR</semantics>∗) of the PBA method is 17.83% higher on average, and the attack success rate (<semantics>ASR</semantics>) is 20.84% higher. It is proven that PBA possesses the best attack capability as well as the highest cross-prompt transferability. Additionally, we introduce a metric to evaluate the cross-prompt transferability of adversarial attacks, effectively fostering research on cross-prompt attacks. Our work unveils the pivotal role of the batched prompts technique in cross-prompt adversarial attacks, marking an early and intriguing exploration into this area against SAM.