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• Vision-language models (VLMs) have inherent biases toward semantic plausibility rather than authenticity, which can negatively impact image forgery detection and localization performance.technical 85%
• Using location masks that explicitly encode forgery concepts as extra priors for VLMs eases their training optimization and enhances interpretability of detection and localization results.method 90%
• The proposed IFDL-VLM pipeline achieves new state-of-the-art performance in detection, localization, and interpretability on 9 popular benchmarks.result 90%
• The IFDL-VLM method was evaluated under both in-domain and cross-dataset generalization settings.method 90%
• The approach may require fine-tuning for different types of forgeries or new types of AI-generated content.limitation 80%
• The method relies on the quality of training data.limitation 80%
• The solution could replace traditional image verification methods and weak AI models, offering superior accuracy and interpretability.market 70%
• The framework significantly outperforms previous methods on key metrics like IoU.result 85%

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