In this paper, we tackle the challenge of white-box false positive adversarial attacks on contrastive loss-based offline handwritten signature verification models. We propose a novel attack method that treats the attack as a style transfer between closely related but distinct writing styles. To guide the generation of deceptive images, we introduce two new loss functions that enhance the attack success rate by perturbing the Euclidean distance between the embedding vectors of the original and synthesized samples, while ensuring minimal perturbations by reducing the difference between the generated image and the original image. Our method demonstrates state-of-the-art performance in white-box attacks on contrastive loss-based offline handwritten signature verification models, as evidenced by our experiments. The key contributions of this paper include a novel false positive attack method, two new loss functions, effective style transfer in handwriting styles, and superior performance in white-box false positive attacks compared to other white-box attack methods.

本文提出了一种新颖的攻击方法，将攻击视为相关但不同书写风格之间的风格转换，通过引入两个新的损失函数来指导欺骗性图像的生成，从而提高成功攻击率并确保最小扰动；我们的方法在白盒攻击的离线手写签名验证模型中表现出卓越的性能。

基于对比损失的离线手写签名验证模型的白盒误报对抗攻击方法