Summary of Anypattern: Towards In-context Image Copy Detection, by Wenhao Wang et al.
AnyPattern: Towards In-context Image Copy Detection
by Wenhao Wang, Yifan Sun, Zhentao Tan, Yi Yang
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the concept of in-context learning for image copy detection (ICD), where an ICD model is prompted to identify replicated images with new tampering patterns without requiring additional training. The prompts come from a small set of image-replica pairs that reflect the new patterns and are used at inference time. This approach has realistic value, as it allows for fast reaction against unseen patterns without the need for fine-tuning. To accommodate the “seen → unseen” generalization scenario, the authors construct the AnyPattern dataset, which includes a large number of tamper patterns. The paper benchmarks AnyPattern with popular ICD methods and reveals that existing methods struggle to generalize to novel patterns. A simple in-context ICD method named ImageStacker is proposed, which learns to select representative image-replica pairs and employs them as pattern prompts in a stacking manner. Experimental results show that training with the large-scale dataset substantially benefits pattern generalization (+26.66% μAP), while the proposed ImageStacker facilitates effective in-context ICD (+16.75% μAP). Additionally, the AnyPattern dataset enables in-context ICD without requiring the large-scale dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching a computer to recognize copied images with new changes without needing more training. The idea is to give the computer some examples of changed images and then use those same examples to identify similar changes later on. This can be useful for quickly identifying new types of image tampering. To make this work, the researchers created a big dataset called AnyPattern that includes many different ways that images can be changed. They tested their method with other popular image detection methods and found that it worked better than those methods at recognizing new changes. The researchers also showed that their method could be used to identify when someone is copying an artist’s style using computer-generated images. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Inference
