The paper proposes a decentralized, privacy-aware framework enabling smart cameras to autonomously coordinate their view coverage in public spaces while explicitly excluding sensitive regions, achievi…

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…

ComPrivDet is an efficient object detection method that detects privacy objects in compressed video streams by reusing inference results from I-frames, significantly reducing latency and computational…

The paper proposes a Class-Aware Adaptive Differential Privacy (CA-ADP) framework integrated with a 3D CNN-BiLSTM architecture to significantly improve privacy-preserving fall detection performance co…

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 PrivHAR-Bench, a multi-tier benchmark dataset that standardizes the evaluation of the privacy-utility trade-off in video-based action recognition by applying a graduated spectrum…

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 introduces PHTV-Scout, a novel framework that analyzes Douyin and Kwai data, revealing a high prevalence of potentially harmful teen videos, particularly CSE imagery, and demonstrating that…

The paper introduces a novel byte-level method to encode network flow records into fixed-size RGB images, significantly improving the performance of Intrusion Detection Systems (IDS) by allowing convo…

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…

EdgeDetect is a communication-efficient and privacy-preserving federated intrusion detection system that uses gradient binarization and homomorphic encryption to significantly reduce bandwidth usage w…

The paper proposes a privacy-preserving visual monitoring system that performs object detection and generates natural language alerts entirely on an edge device, ensuring GDPR compliance by never tran…

The paper proposes a novel, locally deployable agentic workflow using large language models (LLMs) to accurately and privately detect various types of personally identifiable information (PII) within…

SAFEDREAM introduces a lightweight, external world-model framework that proactively detects multi-turn jailbreak attacks by modeling cumulative safety erosion and predicting early failure points.

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 introduces the Street-legal Physical Adversarial Rim (SPAR), a physically realizable and street-legal white-box attack that significantly degrades the accuracy of modern Automatic License Pl…

The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

The paper reverse-engineers Apple's Private Cloud Compute (PCC) implementation to independently benchmark its model and evaluate its privacy claims, addressing the lack of transparency in Apple's syst…

This paper introduces a unified threat model and evaluation framework to systematically compare privacy-preserving techniques for distributed learning in IoT systems, highlighting the trade-off betwee…

The paper proposes CFE-PPAR, the first compression-friendly encryption method for privacy-preserving action recognition, allowing video transformers to recognize actions directly from compressed, encr…