ReAlign distills LLM-generated reasoning texts into a lightweight AIGI forgery detector via contrastive image-text alignment to improve generalization on complex forgeries.
Legion: Learning to ground and explain for synthetic image detection
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
ForenAgent lets MLLMs create and iteratively improve low-level Python tools for image forgery detection via a two-stage training pipeline and a new 100k-image benchmark dataset.
FakeReasoning is an MLLM-based framework for unified forgery detection and reasoning on AI-generated images, supported by the new MMFR-Dataset of 120K images and 378K annotations across 10 generators.
TruEye presents a mask-conditioned dual-stream transformer for fine-grained five-category detection and localization of AI-generated humans in images, claiming superior accuracy and speed over prior detectors on six datasets plus a new FineSyn dataset.
Skyra is an MLLM that detects AI-generated videos by identifying and reasoning over grounded visual artifacts, supported by a new annotated dataset and benchmark.
Omni-Fake delivers a unified multimodal deepfake benchmark dataset and RL-driven detector that reports gains in accuracy, cross-modal generalization, and explainability over prior baselines.
UniGenDet unifies generative and discriminative models through symbiotic self-attention and detector-guided alignment to co-evolve image generation and authenticity detection.
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Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection
ForenAgent lets MLLMs create and iteratively improve low-level Python tools for image forgery detection via a two-stage training pipeline and a new 100k-image benchmark dataset.