ForeAgent combines a Perception-Verdict MLLM architecture with hindsight-driven self-refining via sampling-reflection-evolution to reach 82.18% accuracy on Chameleon and 93.3% mean accuracy across 16 generators on AIGCDetect-Benchmark.
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Patchcraft: Exploring texture patch for efficient ai-generated image detection
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Introduces the Impostor benchmark dataset for localizing AIGC image manipulations via agent curation and the PANet model that uses phase and semantic consistency for better detection.
FiSeR uses coarse contrastive separation of natural vs synthetic images plus fine contrastive grouping by generator identity to improve cross-domain AUROC by +10.22 over DIRE baseline on multiple test sets.
PROBE improves AIGI detector generalization to unseen generators by using the detector as a critic to steer manifold-level modifications that produce challenging training samples.
ReAlign distills LLM-generated reasoning texts into a lightweight AIGI forgery detector via contrastive image-text alignment to improve generalization on complex forgeries.
ImageAttributionBench is a benchmark dataset demonstrating that state-of-the-art image attribution methods lack robustness to image degradation and fail to generalize to semantically disjoint domains.
LEGO uses multiple generator-specific LoRA modules modulated by an MLP and fused with attention to detect synthetic images, achieving better performance than prior methods while using under 10% of the training data.
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.
ForensicConcept extracts and transfers forensic concepts from AIGI detectors via Transformer attribution, concept codebooks, CleanDIFT references, and CKNNA alignment to improve detection on unseen generators.
DetectZoo is a unified toolkit providing reference implementations of 61 detectors, native loaders for 22 benchmark datasets, and a standardized evaluation pipeline for AI-generated content detection across text, audio, and image modalities.
Color transformations expose statistical discrepancies in synthetic images, supporting a classifier with 93.27% average accuracy and robustness to post-processing.
CoDA is a lightweight detector using a Noise-Quantization Probe on color non-uniformity that reports strong cross-domain results on the new FakeForm benchmark and competitive cross-model performance on standard tests.
STAL transfers spectral tail uplift cues via a frequency teacher to train a spatial detector for AI-generated images, discarding frequency modules at inference for strong cross-generator generalization.
SEF introduces GAN upsampling for diverse artifacts and expert fusion to reduce domain interference, yielding stronger generalization on 13 benchmarks for AI-generated image detection.
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
A multi-agent forensic system integrates multiple evidence sources and debate to detect AI-generated images, reporting 97.05% accuracy on a 6,000-image benchmark while outperforming traditional classifiers.
PGC introduces peak-focusing aggregation of local discriminative clues to calibrate global representations for AI-generated image detection, reporting accuracy gains on a new 15-model commercial benchmark and standard datasets.
DeFakerOne is a unified foundation model for joint image-level fake image detection and pixel-level localization that reports SOTA results on 39 detection and 9 localization benchmarks.
MDMF detects AI-generated images by learning patch-level forensic signatures and quantifying their distributional discrepancies with MMD, yielding larger separation than global methods when micro-defects are present.
Researchers train AI detectors on a large photorealistic fake image dataset, apply 16 XAI methods, and use human survey feedback to assess alignment between machine explanations and human perception of AI-generated images.
HiMix combines mixup augmentation to create transitional real-fake samples with hierarchical global-local artifact feature fusion to achieve better generalization in detecting AI-generated images from unseen generators.
FGINet uses a band-masked frequency encoder and layer-wise gated injection to fuse frequency artifacts with vision foundation model semantics, plus hyperspherical compactness learning, to achieve better generalization in AI-generated image detection.
Modern vision foundation models plus a tunable attention pooling classifier head deliver state-of-the-art detection of AI-generated and inpainted images, outperforming CLIP by over 12 percent accuracy.
FakeVLM-R1 combines GRPO reinforcement learning with critical-thinking CoT and a physics-annotated FakeClue++ dataset to reach claimed SOTA synthetic image detection while reducing over-rejection of real images.
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