This study provides the first large-scale analysis of video piracy on Telegram, quantifying its massive financial impact and developing a resilient detection framework, Anti-RIP, to combat it.

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 study finds exploratory evidence that gender moderates how youth perceive privacy risks and benefits, influencing their protective behavior when using smart voice assistants.

This study provides a longitudinal analysis of dark web content, revealing that cybercrime discussions are dominated by a few persistent core topics rather than rapidly shifting themes.

KidsNanny is a two-stage multimodal content moderation pipeline that achieves high accuracy and efficiency in detecting child safety threats, particularly excelling in text-embedded content.

This study proposes a negotiation framework, using composite indices (RBTI and CATI), to explain how youth navigate competing privacy pressures when using smart voice assistants, finding that high usa…

The paper demonstrates that generative AI can automate and scale highly personalized, context-aware spear-phishing attacks using only public social media data, resulting in messages that are significa…

The paper introduces a generalized zero-shot benchmark for facial age estimation that ethically excludes children's data during training, demonstrating that current state-of-the-art models fail signif…

The paper introduces musicPIIrate, a novel tool that demonstrates how Offensive AI can infer sensitive user attributes (like age, gender, and personality) from public music playlists, and proposes Jam…

The paper introduces the PrivacyIceberg framework to systematically categorize and empirically demonstrate the high risk of automated, deep personal profiling using LLM agents, revealing a significant…

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…

This study profiles user vulnerability to phishing by identifying key psychological and behavioral factors, revealing that most users are high-risk due to hasty decision-making rather than lacking tec…

The paper demonstrates that even a casual attacker with basic IT skills can perform sophisticated privacy attacks on smart-home networks, extracting detailed daily routines and personal information fr…

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 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 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 introduces the PROMPT framework to systematically analyze and mitigate privacy risks in online propaganda detection pipelines, demonstrating that current widely used methods are often non-co…

The paper introduces WebPII, a novel, large-scale synthetic benchmark for detecting personally identifiable information (PII) in web screenshots, and demonstrates a model (WebRedact) that significantl…

The paper proposes an on-device framework to detect and prevent the forwarding of images that have been physically recaptured (photographed) from a mobile screen, addressing the Screen Recaptured Anal…