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…

RealityTest introduces a large-scale, multimodal, and multilingual benchmark using real-world human data to test how AI systems disclose their identity, finding that context and phrasing are more crit…

The paper argues that deepfake detection research is misaligned because it focuses on historical threats (public-figure face-swaps) while ignoring the dominant, emerging harms like NCII, voice-cloning…

The paper proposes two novel CAPTCHA types—ASCII art and overlapping audio—and demonstrates that current frontier LLMs struggle significantly to solve them, suggesting they are highly effective anti-b…

This paper systematically diagnoses the failure modes of linear deception probes in LLMs, finding that while single-direction probes are insufficient, multi-dimensional probes can recover robust detec…

The paper introduces a new dataset (SHDF) and a framework (T-AVFD) to robustly detect audio-visual deepfakes, specifically addressing the challenge posed by singing vocalizations.

The study demonstrates that robust, domain-invariant representations of synthetic deception can be rapidly entrenched in LLMs using modest fine-tuning, detectable by linear probes even in early layers…

The paper introduces V.O.I.C.E, a novel, empirically grounded risk taxonomy that comprehensively models the diverse privacy, security, and governance risks associated with the unconsented synthesis an…

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.

This paper investigates whether large language models exhibit uncertainty signals similar to human judgment, examining both overt behavior and internal activation patterns to assess alignment and cali…

This paper investigates if upper-face affective cues enhance audiovisual sentence recognition, especially when audio is degraded, finding that while mouth cues are crucial for robustness, upper-face c…

The paper introduces DECKER, a domain-invariant framework that significantly improves cross-keyboard keystroke inference by normalizing device variations and leveraging linguistic context, demonstrati…

This study demonstrates that Chain-of-Thought (CoT) monitoring is fundamentally fragile and unreliable for detecting misaligned behavior across typologically diverse languages, especially in low-resou…

The paper proposes an evolutionary multi-objective score fusion framework that efficiently combines multiple deepfake speech detectors to achieve state-of-the-art accuracy while significantly reducing…

The paper introduces Rate Matching Consistency Training (RMCT), a novel method that improves model robustness against extraneous input cues without forcing the model to ignore those cues, thus preserv…