This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…

The paper demonstrates that off-the-shelf image diffusion models, like Stable Diffusion, can be repurposed to generate synthetic structured data, posing a threat of ground truth drift in closed eviden…

The paper introduces a theoretically grounded evaluation framework for watermarking generative models, proposing a novel method (SSB) that allows for systematic design across all security-robustness-f…

The paper analyzes the real threat of GenAI in cybercrime, arguing that while high-end automation (Stand-Alone Complex) is possible, current adoption is low and primarily affects skilled actors, sugge…

The paper proposes FLAME, a novel framework that detects AI-generated image forgeries by identifying intrinsic energy anomalies caused by the diffusion process, achieving state-of-the-art localization…

The paper proposes a black-box attack using a hierarchical genetic algorithm to induce 'overthinking' in Large Reasoning Models, demonstrating that this vulnerability can cause significant resource ex…

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…

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…

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 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…

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…

MemMark introduces a state-evolution attribution watermark that embeds owner-controlled signals into latent memory-write decisions, enabling robust provenance tracking for agent memory even when all t…

This paper proposes using color statistics, specifically through novel color transformations, to detect AI-generated synthetic images by exploiting the color-imitation weaknesses of current generative…

PHANTOM is a novel framework that generates highly convincing, context-aware honeytokens by incorporating deep organizational knowledge, significantly improving their believability and detection resis…

This paper models the security risks of subagent spawning in multi-agent networks, demonstrating that insecure memory inheritance from parent agents allows local compromises to spread across system bo…

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…

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 paper quantifies the polymorphic capacity of a commercial LLM, demonstrating that it can cheaply generate large populations of structurally diverse, yet behaviorally equivalent, offensive code pa…