Mark My Words: Dangers of Watermarked Images in ImageNet
Kirill Bykov, Klaus-Robert M\"uller, Marina M.-C. H\"ohne

TL;DR
This paper investigates how watermarks in ImageNet images influence pre-trained neural networks, revealing widespread effects across multiple classes and proposing a mitigation method to reduce watermark-related artifacts in feature representations.
Contribution
It demonstrates that watermarks affect various ImageNet classes and introduces a simple technique to mitigate watermark artifacts in fine-tuned models.
Findings
Watermarks influence multiple ImageNet classes beyond the 'carton' class.
Pre-trained networks encode watermark patterns in their features.
Ignoring watermark-affected features improves model robustness.
Abstract
The utilization of pre-trained networks, especially those trained on ImageNet, has become a common practice in Computer Vision. However, prior research has indicated that a significant number of images in the ImageNet dataset contain watermarks, making pre-trained networks susceptible to learning artifacts such as watermark patterns within their latent spaces. In this paper, we aim to assess the extent to which popular pre-trained architectures display such behavior and to determine which classes are most affected. Additionally, we examine the impact of watermarks on the extracted features. Contrary to the popular belief that the Chinese logographic watermarks impact the "carton" class only, our analysis reveals that a variety of ImageNet classes, such as "monitor", "broom", "apron" and "safe" rely on spurious correlations. Finally, we propose a simple approach to mitigate this issue in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Advanced Steganography and Watermarking Techniques
