The paper introduces MARS, a novel meta-adversarial framework that significantly improves black-box adversarial attacks against state-of-the-art Singing Voice Deepfake Detection (SVDD) systems by esca…

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 study investigates how humans detect synthetic speech in real-world contexts, finding that while overt detection failed for fully synthetic speech, participants still implicitly discriminated utt…

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…

The paper proposes the Shortcut Subspace Suppression (S^3) framework to improve deepfake detection generalization by explicitly identifying and suppressing method-specific shortcuts in learned feature…

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…

GAFSV-Net introduces a novel 2D vision framework by encoding temporal signature data into a six-channel Gramian Angular Field image, significantly improving online signature verification accuracy over…

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

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 Multi-Clip Video (MCV) SafetyBench, a dataset demonstrating that the vulnerability of Multimodal Large Language Models (MLLMs) to jailbreaking increases with the diversity and num…

The paper introduces AudioHijack, a framework that successfully demonstrates context-agnostic and imperceptible auditory prompt injection attacks, showing that commercial Large Audio-Language Models c…

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 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…

DeepFake Forensics AI is a novel, multi-modal platform that detects synthetic media across image, video, and audio, while simultaneously ensuring tamper-proof evidence management using blockchain tech…

The paper demonstrates that passive motion traces recorded during a mobile selfie capture can serve as a measurable, low-friction auxiliary signal for enhancing both spoof screening and user identity…

The paper proposes a sequence-alignment framework using Soft Dynamic Time Warping to evaluate audio-driven talking-head generation, demonstrating that this approach provides more robust and fair compa…