Watermark Faker: Towards Forgery of Digital Image Watermarking

TL;DR

This paper introduces a novel deep learning-based method to generate fake watermarked images, exposing vulnerabilities in digital watermarking systems against forgery attacks.

Contribution

It pioneers the use of generative adversarial networks to create fake watermarked images, highlighting a new security threat in digital watermarking.

Findings

Watermark faker effectively cracks existing watermarking methods.

The approach works in both spatial and frequency domains.

Demonstrates the potential risk of forgery attacks on digital watermarks.

Abstract

Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the emerging deep learning based image generation technology raises new open issues that whether it is possible to generate fake watermarked images for circumvention. In this paper, we make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Suppose that a set of paired images of original and watermarked images generated by the targeted watermarker are available, we use them to train a watermark faker with U-Net as the backbone, whose input is an original image, and after a domain-specific preprocessing, it outputs a fake watermarked image. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains, suggesting the risk of such forgery attacks.