Dimensions overall score 7.0

• Existing image forgery localization (IFL) methods struggle to localize manipulations in fully regenerated (FR) images, while synthetic image detection (SID) methods can detect FR images but cannot perform localization.limitation 90%
• TGIF2 is an extended dataset that includes manipulations generated by FLUX.1 models and random non-semantic masks.method 95%
• Both IFL and SID methods degrade in performance on manipulations created by FLUX.1 models, highlighting limited generalization.result 90%
• Fine-tuning IFL methods on FR images improves localization performance on FR images.result 85%
• Evaluation using random non-semantic masks reveals that fine-tuned IFL methods exhibit object bias.limitation 90%
• Generative super-resolution attacks significantly weaken forensic traces, undermining current forensic pipelines.result 90%
• TGIF2 uses the MS-COCO dataset (val2017) as its source of authentic images, excluding the 'knife' and 'frisbee' categories due to Adobe Firefly restrictions.method 95%
• FLUX.1 [schnell] and FLUX.1 [dev] models were trained for full synthetic image generation, not specifically fine-tuned for inpainting, yet can be used in an inpainting workflow.technical 90%

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Startup potential card