Rethinking Adversarial Examples for Location Privacy Protection
We have investigated a new application of adversarial examples, namely location privacy protection against landmark recognition systems. We introduce mask-guided multimodal projected gradient descent (MM-PGD), in which adversarial examples are trained on different deep models. Image contents are protected by analyzing the properties of regions to identify the ones most suitable for blending in adversarial examples. We investigated two region identification strategies: class activation map-based MM-PGD, in which the internal behaviors of trained deep models are targeted; and human-vision-based MM-PGD, in which regions that attract less human attention are targeted. Experiments on the Places365 dataset demonstrated that these strategies are potentially effective in defending against black-box landmark recognition systems without the need for much image manipulation.
Code (0)
등록된 구현이 없습니다.
Tasks
Image ManipulationLandmark RecognitionSimilar Papers 제목 키워드 기반
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks
Malicious intelligent algorithms greatly threaten the security of social users' privacy by detecting and analyzing the uploaded photos to social network platforms. The destruction to DNNs brought by the adversarial attac…
Adversarial AttackAdversarial DefenseGenerative Adversarial NetworkErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models
While face recognition (FR) models have brought remarkable convenience in face verification and identification, they also pose substantial privacy risks to the public. Existing facial privacy protection schemes usually a…
Adversarial AttackFace RecognitionFace VerificationImperceptible and Reversible Adversarial Examples against Vision-Language Models for Privacy Protection
Vision Language Models (VLMs) offer powerful multimodal ability but also expose users to text-based privacy attacks where adversaries crawl online photos and query VLMs to extract sensitive attributes. Existing reversibl…
Beyond Pixels: Semantic-aware Typographic Attack for Geo-Privacy Protection
Large Visual Language Models (LVLMs) now pose a serious yet overlooked privacy threat, as they can infer a social media user's geolocation directly from shared images, leading to unintended privacy leakage. While adversa…
GeoShield: Safeguarding Geolocation Privacy from Vision-Language Models via Adversarial Perturbations
Vision-Language Models (VLMs) such as GPT-4o now demonstrate a remarkable ability to infer users' locations from public shared images, posing a substantial risk to geoprivacy. Although adversarial perturbations offer a p…