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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- Linked via arxiv authorKutub Uddin →
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
- Linked via arxiv authorNusrat Tasnim →
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
- Linked via arxiv authorAwais Khan →
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
- Linked via arxiv authorMohammad Umar Farooq →
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
- Linked via arxiv authorKhalid Malik →
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection