ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912456966668288 |
|---|---|
| author | Chen, Yingjian Zhang, Lei Niu, Yakun |
| author_facet | Chen, Yingjian Zhang, Lei Niu, Yakun |
| contents | The rise of generative models has raised concerns about image authenticity online, highlighting the urgent need for a detector that is (1) highly generalizable, capable of handling unseen forgery techniques, and (2) data-efficient, achieving optimal performance with minimal training data, enabling it to counter newly emerging forgery techniques effectively. To achieve this, we propose ForgeLens, a data-efficient, feature-guided framework that incorporates two lightweight designs to enable a frozen network to focus on forgery-specific features. First, we introduce the Weight-Shared Guidance Module (WSGM), which guides the extraction of forgery-specific features during training. Second, a forgery-aware feature integrator, FAFormer, is used to effectively integrate forgery information across multi-stage features. ForgeLens addresses a key limitation of previous frozen network-based methods, where general-purpose features extracted from large datasets often contain excessive forgery-irrelevant information. As a result, it achieves strong generalization and reaches optimal performance with minimal training data. Experimental results on 19 generative models, including both GANs and diffusion models, demonstrate improvements of 13.61% in Avg.Acc and 8.69% in Avg.AP over the base model. Notably, ForgeLens outperforms existing forgery detection methods, achieving state-of-the-art performance with just 1% of the training data. Our code is available at https://github.com/Yingjian-Chen/ForgeLens. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13697 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection Chen, Yingjian Zhang, Lei Niu, Yakun Computer Vision and Pattern Recognition The rise of generative models has raised concerns about image authenticity online, highlighting the urgent need for a detector that is (1) highly generalizable, capable of handling unseen forgery techniques, and (2) data-efficient, achieving optimal performance with minimal training data, enabling it to counter newly emerging forgery techniques effectively. To achieve this, we propose ForgeLens, a data-efficient, feature-guided framework that incorporates two lightweight designs to enable a frozen network to focus on forgery-specific features. First, we introduce the Weight-Shared Guidance Module (WSGM), which guides the extraction of forgery-specific features during training. Second, a forgery-aware feature integrator, FAFormer, is used to effectively integrate forgery information across multi-stage features. ForgeLens addresses a key limitation of previous frozen network-based methods, where general-purpose features extracted from large datasets often contain excessive forgery-irrelevant information. As a result, it achieves strong generalization and reaches optimal performance with minimal training data. Experimental results on 19 generative models, including both GANs and diffusion models, demonstrate improvements of 13.61% in Avg.Acc and 8.69% in Avg.AP over the base model. Notably, ForgeLens outperforms existing forgery detection methods, achieving state-of-the-art performance with just 1% of the training data. Our code is available at https://github.com/Yingjian-Chen/ForgeLens. |
| title | ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.13697 |