arXiv AI
Jun 30
IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting
arXiv:2606. 30024v1 Announce Type: cross Abstract: Recent advances in deep learning have notably improved steganographic message hiding.
PrinGS: Privacy Preservation in Gaussian Splatting
PrinGS introduces a privacy‑preserving framework for 3D Gaussian Splatting, enabling post‑training anonymization of sensitive content such as faces, license plates, documents, and computer screens. By localizing and directly anonymizing regions in the 3D representation, the method removes sensitive information from all rendered novel views while maintaining scene geometry and visual fidelity, achieving this in under one second. The approach also offers a privacy‑by‑design training strategy that prevents sensitive data from being encoded during optimization, yielding splats with low face similarity and unreadable text.
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arXiv AI
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Vision Language Model Helps Private Information De-Identification in Vision Data
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arXiv Computer Vision
Aug 26
Source-Face Authenticity Detection for 3D Gaussian Heads Reconstructed from a Single Portrait: A Benchmark and Dedicated Detector
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arXiv AI
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Guided Zero-Shot Anomaly Explanation for Anonymized Crowd Video Monitoring: Context, Categorization, and Spatial Localization
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arXiv AI
Jul 17
Benchmarking Face Recognition without Real Faces
arXiv:2607. 14932v1 Announce Type: cross Abstract: Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs.
arXiv Machine Learning
Aug 20
On the Robustness of Vision-Language Models in Zero-shot Privacy Classification
The paper investigates whether large, instruction‑following Vision‑Language Models (VLMs) can reliably perform zero‑shot image privacy classification. It compares three open‑source VLMs to specialized privacy models on two public benchmarks, evaluating accuracy, robustness to image degradations (compression, lighting changes, noise), inference speed, and parameter count. The findings show that while VLMs remain robust to perturbations, they are less accurate and significantly slower than smaller, purpose‑built privacy models, indicating that scaling alone does not guarantee effective privacy classification.
Hugging Face Trending Papers
Jul 16
Benchmarking Face Recognition without Real Faces
Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace.
arXiv AI
Jul 21
Privacy-Aware Synthetic Video Benchmarking and Relational Evaluation for Worker-Under-Suspended-Load Detection
arXiv:2607. 16351v1 Announce Type: cross Abstract: Publicly shareable construction-video benchmarks remain scarce, especially for safety-critical hazards that are rare, dangerous to stage, and difficult to release.
arXiv Computer Vision
Aug 24
Cloak of Invisibility: Real-Time Privacy-Preserving Volumetric Video Streaming
arXiv:2608.11645v2 Announce Type: replace Abstract: Volumetric video streaming turns privacy into a 3D, multi-view problem. Unlike ordinary video, where sensitive content can often be redacted frame...
arXiv AI
Sep 1
Defending Wearable VLMs Against Private Attribute Inference
arXiv:2608.28691v1 Announce Type: cross Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surround...
arXiv Computer Vision
Oct 8
How Private is Private? A Comparative Study for Face De-Identification
The paper introduces a unified evaluation framework for face de-identification (FDeID), addressing inconsistencies in current protocols. It presents UtilFace, a demographically balanced benchmark assembled from four large-scale datasets, and HiFD, a hierarchical metric that simultaneously measures identity suppression, multi-level utility preservation, and image quality. Using this framework, the authors compare adversarial, GAN-based, and diffusion-based methods, revealing trade-offs and failure modes that were previously hidden, and they release the benchmark and toolkit for reproducible research.
arXiv AI
Jul 31
FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking
arXiv:2607. 26641v1 Announce Type: cross Abstract: Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns.