Ulixes: Facial Recognition Privacy with Adversarial Machine Learning

Abstract

Facial recognition tools are becoming exceptionally accurate in identifying people from images. However, this comes at the cost of privacy for users of online services with photo management (e.g. social media platforms). Particularly troubling is the ability to leverage unsupervised learning to recognize faces even when the user has not labeled their images. In this paper we propose Ulixes, a strategy to generate visually non-invasive facial noise masks that yield adversarial examples, preventing the formation of identifiable user clusters in the embedding space of facial encoders. This is applicable even when a user is unmasked and labeled images are available online. We demonstrate the effectiveness of Ulixes by showing that various classification and clustering methods cannot reliably label the adversarial examples we generate. We also study the effects of Ulixes in various black-box settings and compare it to the current state of the art in adversarial machine learning. Finally, we challenge the effectiveness of Ulixes against adversarially trained models and show that it is robust to countermeasures.

References in corpus (20)

• FaceNet: A Unified Embedding for Face Recognition and Clustering
• Explaining and Harnessing Adversarial Examples
• Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
• Deep Learning Face Representation by Joint Identification-Verification
• Learning Face Representation from Scratch
• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
• DeepID3: Face Recognition with Very Deep Neural Networks
• Delving into Transferable Adversarial Examples and Black-box Attacks
• Robust Face Recognition via Multimodal Deep Face Representation
• Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization
• Adversarial Machine Learning at Scale
• Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
• Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
• VGGFace2: A dataset for recognising faces across pose and age
• Triplet Probabilistic Embedding for Face Verification and Clustering
• Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
• Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
• Triplet Similarity Embedding for Face Verification
• LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition