mlsanityData to 7 Oct 2026

Watermark Faker: Towards Forgery of Digital Image Watermarking

arXiv 2103.12489 ↗First posted 23 Mar 2021cs.CR cs.AI cs.CVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 23 Mar 2021.

Builds on

13 of 24 references.

References · 13

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2014Conditional Generative Adversarial Netscited by 2,152
  3. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  4. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
  5. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
  6. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  7. 2016Least Squares Generative Adversarial Networkscited by 1,000
  8. 2018HiDDeN: Hiding Data With Deep Networkscited by 384
  9. 2018Diverse Image-to-Image Translation via Disentangled Representationscited by 296
  10. 2017Toward Multimodal Image-to-Image Translationcited by 295
  11. 2020Cross-domain Correspondence Learning for Exemplar-based Image Translationcited by 116
  12. 2021A Brief Survey on Deep Learning Based Data Hidingcited by 9
1 more
  1. 2020Steganography GAN: Cracking Steganography with Cycle Generative Adversarial Networkscited by 2

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Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 3.

7 papers.

  1. 2024Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Modelscited by 48
  2. 2024How to Trace Latent Generative Model Generated Images without Artificial Watermark?cited by 25
  3. 2025Forging and Removing Latent-Noise Diffusion Watermarks Using a Single Imagecited by 12
  4. 2025Transferable Black-Box One-Shot Forging of Watermarks via Image Preference Modelscited by 10
  5. 2023Warfare:Breaking the Watermark Protection of AI-Generated Contentcited by 7
  6. 2025WMCopier: Forging Invisible Image Watermarks on Arbitrary Imagescited by 5
  7. 2024Certifiably Robust Image Watermarkcited by 3