  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.
