The paper proposes an end-to-end forensic pipeline using steganographic attribution and multimodal harm detection to reliably trace and attribute harmful misuse of AI-generated imagery on social platf…

The paper proposes a unified evidentiary framework combining cryptographic provenance, statistical watermarking, and zero-knowledge attestation to address the legal challenges posed by synthetic media…

The paper demonstrates that current AI watermark removal techniques fail to achieve true forensic stealth, as the removal process often leaves behind detectable signals that distinguish the output fro…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

This paper proposes a 3D CNN detector that leverages temporal artifacts to accurately identify high-quality deepfake videos, demonstrating robust detection even after social media re-encoding.

The paper proposes a unified, architecture-agnostic framework that significantly improves the robustness of deepfake image detectors against adversarial attacks by focusing on higher-order frequency s…

This paper addresses the challenge of detecting and explaining AI-manipulated segments within long, untrimmed videos by proposing a new benchmark and a coarse-to-fine forensic detection framework.

DeepSignature proposes a novel, cryptographically verifiable watermarking system that uses deep neural networks to embed digital signatures into images, enabling robust source attribution and near 100…

This paper investigates how Generative AI enables scalable, hyper-realistic fraud in Chinese e-commerce by fabricating product defect evidence, proposing new defense mechanisms like verifiable materia…

GenDetect introduces a novel framework to rapidly generalize detection rules from single observed DeFi exploits, significantly improving resilience against subsequent, similar 'Imitative Attack Cascad…

The paper introduces LOCARD, an agentic framework that models blockchain forensics as a sequential decision-making process, demonstrating its effectiveness in complex cross-chain transaction tracing.

TimeMark proposes a trustworthy time watermarking framework that uses cryptographic techniques and error-correcting codes to achieve 100% accurate recovery of the generation time from AIGC, resisting…

The paper introduces SEED, a large-scale benchmark dataset for tracing sequential deepfake facial edits, and proposes FAITH, a frequency-aware Transformer model that effectively detects and orders the…

The paper introduces Synthetic Trust Attacks (STAs) as a formal threat category, arguing that AI fraud targets the victim's decision-making process rather than just synthetic media, and proposes a dec…

This paper conducts an independent security analysis of the C2PA specifications and concludes that the current system fails to meet its claimed security and necessary functional goals, making it unrel…

This study comparatively evaluates four CNN architectures (VGG16, ResNet50, EfficientNetB0, and XceptionNet) for fake image detection, finding VGG16 achieved the highest accuracy (91%).

The paper introduces FraudBench, a multimodal benchmark designed to detect AI-generated fraudulent refund evidence, finding that current AI models struggle significantly with claim-conditioned fake-da…

UniDetect is a novel LLM-driven method that detects cross-chain cryptocurrency fraud by generating generalized transaction summaries, significantly outperforming existing detection techniques across m…