BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning (2108.00352v1)

Abstract: Self-supervised learning in computer vision aims to pre-train an image encoder using a large amount of unlabeled images or (image, text) pairs. The pre-trained image encoder can then be used as a feature extractor to build downstream classifiers for many downstream tasks with a small amount of or no labeled training data. In this work, we propose BadEncoder, the first backdoor attack to self-supervised learning. In particular, our BadEncoder injects backdoors into a pre-trained image encoder such that the downstream classifiers built based on the backdoored image encoder for different downstream tasks simultaneously inherit the backdoor behavior. We formulate our BadEncoder as an optimization problem and we propose a gradient descent based method to solve it, which produces a backdoored image encoder from a clean one. Our extensive empirical evaluation results on multiple datasets show that our BadEncoder achieves high attack success rates while preserving the accuracy of the downstream classifiers. We also show the effectiveness of BadEncoder using two publicly available, real-world image encoders, i.e., Google's image encoder pre-trained on ImageNet and OpenAI's Contrastive Language-Image Pre-training (CLIP) image encoder pre-trained on 400 million (image, text) pairs collected from the Internet. Moreover, we consider defenses including Neural Cleanse and MNTD (empirical defenses) as well as PatchGuard (a provable defense). Our results show that these defenses are insufficient to defend against BadEncoder, highlighting the needs for new defenses against our BadEncoder. Our code is publicly available at: https://github.com/jjy1994/BadEncoder.

• The paper demonstrates a backdoor attack that optimizes dual-component loss functions to stealthily redirect pre-trained SSL encoders with minimal impact on performance.
• The proposed method leverages gradient descent and carefully balanced utility and effectiveness losses to achieve near 100% attack success rates across multiple datasets.
• Empirical evaluations reveal the attack's robustness even with mismatched data distributions, challenging existing defenses and highlighting the need for improved SSL security measures.

An Analysis of "BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning"

The paper "BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning" presents a novel security threat to self-supervised learning (SSL) frameworks in computer vision, specifically targeting pre-trained image encoders. The proposed attack, named BadEncoder, aims to exploit vulnerabilities in SSL pipelines, which have become increasingly popular due to their ability to learn useful feature representations from unlabeled data. Given the widespread application of such models in various downstream tasks without requiring extensive labeled datasets, the security implications of these findings are significant.

Theoretical Framework

The authors detail how the BadEncoder attack is formulated as an optimization problem. The objective is to inject a backdoor into a pre-trained encoder such that any downstream classifiers leveraging this compromised encoder also inherit the backdoor functionality. This is achieved by crafting the encoder to maintain high utility for clean inputs while redirecting inputs with an embedded trigger to an attacker-specified target class.

Several critical components underpin the BadEncoder attack:

1. Optimization Problem: The method involves defining a dual-component loss function comprising an effectiveness loss and a utility loss. The effectiveness loss ensures the backdoor trigger correctly redirects features extracted by the encoder, whereas the utility loss aims to preserve encoder performance on non-triggered inputs.
2. Gradient Descent Method: The authors employ a gradient descent-based approach to solve the optimization problem, enabling the generation of a backdoored encoder from a clean one.
3. Adversarial Assumptions: In their threat model, the attacker has access to a set of reference inputs from the target class and some unlabeled images (shadow dataset) that may or may not share the same distribution as the pre-training data.

Empirical Evaluation

In a comprehensive set of experiments using datasets like CIFAR10, STL10, GTSRB, and SVHN, the paper demonstrates the high success rates of the BadEncoder attack while maintaining classification accuracy for clean inputs. The performance of their attack in scenarios where the shadow data distribution differs from that of the pre-training dataset suggests a robustness and generalizability of the proposed method.

Key Findings

• The attack successfully achieves high attack success rates (ASR), often near 100%, indicating the backdoor's effectiveness across different target classes and datasets.
• The authors emphasize the attack's ability to maintain utility, preserving clean accuracy (CA) to a significant extent. Differences between clean and backdoored accuracy are typically minimal, highlighting the stealthiness of BadEncoder.

Implications and Future Directions

The implications of the BadEncoder attack are profound for both theoretical understanding and practical deployment of SSL models. This vulnerability necessitates a reconsideration of trust models in machine learning, especially given the attack's ability to remain undetected by several existing defense mechanisms. The paper demonstrates that standard empirical defenses, like Neural Cleanse and MNTD, fail to adequately detect the BadEncoder, while PatchGuard provides insufficient robustness guarantees.

Future Developments in AI: The findings encourage further exploration into securing SSL frameworks, including improved detection mechanisms to safeguard against these types of attacks. Moreover, this work prompts the development of certifiable defenses capable of providing stronger guarantees without compromising model effectiveness. It also calls for an exploration of training methodologies that inherently resist backdoor embedding.

In conclusion, the paper offers a thorough analysis of backdoor vulnerabilities in SSL and sets a foundational direction for both defense strategies and a deeper understanding of risks in neural network deployments.