Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
The rapid advancement of generative AI has enabled the creation of highly realistic deepfake media, posing significant threats, including misinformation, digital identity theft, fraud, and manipulation of public opinion. AI-generated image (AIGI) detection is reliably challenging due to the diversity of generative methods and the subtle artifacts they leave behind. In this work, we propose GenRes, a novel framework for generative residual learning via a neural tensor network, which models fine-grained relational features between original and transformed samples to enhance generalization. To ad
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- PossiblePossibly related (embedding) · 49%Meta rolls out Muse, a new AI image generator →
- PossiblePossibly related (embedding) · 46%voxel51/fiftyone →
- PossiblePossibly related (embedding) · 46%Meta just launched a new AI generator, Muse Image, and users are already pushing back over use of their photos →
- LinkedLinked via arxiv author · 85%Kutub Uddin →
“Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection”
- LinkedLinked via arxiv author · 85%Nusrat Tasnim →
“Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection”
- LinkedLinked via arxiv author · 85%Awais Khan →
“Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection”
- LinkedLinked via arxiv author · 85%Mohammad Umar Farooq →
“Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection”
- LinkedLinked via arxiv author · 85%Khalid Malik →
“Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection”