A controlled audit on 1,500 images across seven generators finds that training-free detector AUROCs vary by up to 0.38 with backbone choice, preprocessing resolution, and noise sigma, and that score direction is hyperparameter-dependent rather than intrinsic.
Rigid: A training-free and model-agnostic framework for ro- bust ai-generated image detection
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DRIFT learns a structured invariance manifold from real images via one-class supervision on decomposed robust and fragile subspaces of a frozen VFM to detect AI-generated images through margin violations.
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.
HydraPrompt uses an Asymmetric Prompt Adapter with fixed real prompts and adaptive fake prompts plus a Conditional Supervised Contrastive loss to achieve SOTA synthetic image detection on benchmarks.
Intermediate layer embedding sensitivity to perturbations distinguishes AI-generated images from real ones, yielding higher AUROC on GenImage and Forensics Small benchmarks than prior methods.
A training-free dual-system framework refines anomaly score ordering on uncertain samples from self-supervised talking head forgery detectors to improve detection performance.
The ITW-SM dataset and targeted optimization of detector design choices yield a 26.87% average AUC improvement for state-of-the-art AI-generated image detectors under real-world social media conditions.
HFI detects LDM-generated images without training data by quantifying aliasing in autoencoder outputs and supports model-specific implicit watermarking.
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.
Binary AI vs. real image classification reaches F1 > 0.83 while identifying the exact generative model achieves a highest F1 of 0.4986 on the MS COCOAI dataset.
citing papers explorer
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How Fragile Are Training-Free AI-Generated Image Detectors? A Controlled Audit of Score Direction, Preprocessing, and Compression
A controlled audit on 1,500 images across seven generators finds that training-free detector AUROCs vary by up to 0.38 with backbone choice, preprocessing resolution, and noise sigma, and that score direction is hyperparameter-dependent rather than intrinsic.
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DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection
DRIFT learns a structured invariance manifold from real images via one-class supervision on decomposed robust and fragile subspaces of a frozen VFM to detect AI-generated images through margin violations.
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Toward Generalizable Forgery Detection and Reasoning
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.
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HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection
HydraPrompt uses an Asymmetric Prompt Adapter with fixed real prompts and adaptive fake prompts plus a Conditional Supervised Contrastive loss to achieve SOTA synthetic image detection on benchmarks.
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Intermediate Representations are Strong AI-Generated Image Detectors
Intermediate layer embedding sensitivity to perturbations distinguishes AI-generated images from real ones, yielding higher AUROC on GenImage and Forensics Small benchmarks than prior methods.
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Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework
A training-free dual-system framework refines anomaly score ordering on uncertain samples from self-supervised talking head forgery detectors to improve detection performance.
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Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters?
The ITW-SM dataset and targeted optimization of detector design choices yield a 26.87% average AUC improvement for state-of-the-art AI-generated image detectors under real-world social media conditions.
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HFI: A unified framework for training-free detection and implicit watermarking of latent diffusion model generated images
HFI detects LDM-generated images without training data by quantifying aliasing in autoencoder outputs and supports model-specific implicit watermarking.
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Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts
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.
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Findings of the Counter Turing Test: AI-Generated Image Detection
Binary AI vs. real image classification reaches F1 > 0.83 while identifying the exact generative model achieves a highest F1 of 0.4986 on the MS COCOAI dataset.