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arXiv Computer Vision

Privacy-Preserving Object Detection for Vision Transformer-Based Models

The paper introduces a new object detection approach that protects sensitive visual data in test images. It is the first to apply perceptual encryption to object detection, leveraging the embedding structure of Vision Transformers and a key-based domain adaptation to maintain accuracy comparable to unprotected models. Experiments on ViTdet confirm the method’s effectiveness in both accuracy and visual protection.

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arXiv AI
Aug 28

Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformations

The paper argues that evaluating privacy‑enhancing technologies (PETs) solely through image classification is insufficient because classification remains robust to many geometric and local perturbations. It proposes a compute‑aware multi‑task protocol that uses lightweight proxy tasks to assess PETs across various transformations, revealing that PETs with similar classification accuracy can perform very differently on other vision tasks. The study demonstrates the necessity of broader evaluation metrics beyond classification to truly gauge PET effectiveness.

By Leon Ranke, Wolfgang H\"ubner, Ronny Hug, Michael Arens, J\"urgen Beyerer
computer-visionsafety
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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.

By Alina Elena Baia, Alessio Xompero, Andrea Cavallaro
llmsefficiencymultimodalbenchmarkssafety
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Hugging Face Trending Papers
Jun 28

Bit-ViP: Leveraging Bit-planes to Preserve Visual Privacy in Images through Obfuscation

The unprecedented growth of computer vision applications, such as surveillance systems and social media, raises security and visual privacy concerns, especially when data is stored on cloud servers. Image obfuscation offers a way to preserve visual privacy while maintaining an adequate level of usability; thus, it has been a topic of great interest in recent years.

computer-visionsafety
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arXiv Machine Learning
Aug 28

Federated Adversarial Training with Transformers

The paper investigates federated adversarial training (AT) for vision transformers, a topic not previously explored in federated learning (FL). It evaluates various transformer architectures and aggregation strategies, and introduces FedWAvg, an extension of FedAvg that weights client updates based on similarity of their last-layer representations. Experiments demonstrate that FedWAvg yields higher robust accuracy than existing aggregation methods in non‑IID settings.

By Ahmed Aldahdooh, Wassim Hamidouche, Olivier D\'eforges
llmscomputer-visionnlpbenchmarkssafety
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