DiffWA: Diffusion Models for Watermark Attack
Xinyu Li
With the rapid development of deep neural networks(DNNs), many robust blind
watermarking algorithms and frameworks have been proposed and achieved good
results. At present, the watermark attack algorithm can not compete with the
watermark addition algorithm. And many watermark attack algorithms only care
about interfering with the normal extraction of the watermark, and the
watermark attack will cause great visual loss to the image. To this end, we
propose DiffWA, a conditional diffusion model with distance guidance for
watermark attack, which can restore the image while removing the embedded
watermark. The core of our method is training an image-to-image conditional
diffusion model on unwatermarked images and guiding the conditional model using
a distance guidance when sampling so that the model will generate unwatermarked
images which is similar to original images. We conducted experiments on
CIFAR-10 using our proposed models. The results shows that the model can remove
the watermark with good effect and make the bit error rate of watermark
extraction higher than 0.4. At the same time, the attacked image will maintain
good visual effect with PSNR more than 31 and SSIM more than 0.97 compared with
the original image.