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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.05466 |
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| _version_ | 1866909646595293184 |
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| author | Costanzino, Alex Bayliss, Woody Sock, Juil Blanch, Marc Gorriz Horak, Danijela Laptev, Ivan Torr, Philip Pizzati, Fabio |
| author_facet | Costanzino, Alex Bayliss, Woody Sock, Juil Blanch, Marc Gorriz Horak, Danijela Laptev, Ivan Torr, Philip Pizzati, Fabio |
| contents | Changing facial expressions, gestures, or background details may dramatically alter the meaning conveyed by an image. Notably, recent advances in diffusion models greatly improve the quality of image manipulation while also opening the door to misuse. Identifying changes made to authentic images, thus, becomes an important task, constantly challenged by new diffusion-based editing tools. To this end, we propose a novel approach for ReliAble iDentification of inpainted AReas (RADAR). RADAR builds on existing foundation models and combines features from different image modalities. It also incorporates an auxiliary contrastive loss that helps to isolate manipulated image patches. We demonstrate these techniques to significantly improve both the accuracy of our method and its generalisation to a large number of diffusion models. To support realistic evaluation, we further introduce BBC-PAIR, a new comprehensive benchmark, with images tampered by 28 diffusion models. Our experiments show that RADAR achieves excellent results, outperforming the state-of-the-art in detecting and localising image edits made by both seen and unseen diffusion models. Our code, data and models will be publicly available at https://alex-costanzino.github.io/radar/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05466 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Reliable Identification of Diffusion-based Image Manipulations Costanzino, Alex Bayliss, Woody Sock, Juil Blanch, Marc Gorriz Horak, Danijela Laptev, Ivan Torr, Philip Pizzati, Fabio Computer Vision and Pattern Recognition Changing facial expressions, gestures, or background details may dramatically alter the meaning conveyed by an image. Notably, recent advances in diffusion models greatly improve the quality of image manipulation while also opening the door to misuse. Identifying changes made to authentic images, thus, becomes an important task, constantly challenged by new diffusion-based editing tools. To this end, we propose a novel approach for ReliAble iDentification of inpainted AReas (RADAR). RADAR builds on existing foundation models and combines features from different image modalities. It also incorporates an auxiliary contrastive loss that helps to isolate manipulated image patches. We demonstrate these techniques to significantly improve both the accuracy of our method and its generalisation to a large number of diffusion models. To support realistic evaluation, we further introduce BBC-PAIR, a new comprehensive benchmark, with images tampered by 28 diffusion models. Our experiments show that RADAR achieves excellent results, outperforming the state-of-the-art in detecting and localising image edits made by both seen and unseen diffusion models. Our code, data and models will be publicly available at https://alex-costanzino.github.io/radar/. |
| title | Towards Reliable Identification of Diffusion-based Image Manipulations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05466 |