Label-Consistent Backdoor Attacks (1912.02771v2)

Abstract: Deep neural networks have been demonstrated to be vulnerable to backdoor attacks. Specifically, by injecting a small number of maliciously constructed inputs into the training set, an adversary is able to plant a backdoor into the trained model. This backdoor can then be activated during inference by a backdoor trigger to fully control the model's behavior. While such attacks are very effective, they crucially rely on the adversary injecting arbitrary inputs that are---often blatantly---mislabeled. Such samples would raise suspicion upon human inspection, potentially revealing the attack. Thus, for backdoor attacks to remain undetected, it is crucial that they maintain label-consistency---the condition that injected inputs are consistent with their labels. In this work, we leverage adversarial perturbations and generative models to execute efficient, yet label-consistent, backdoor attacks. Our approach is based on injecting inputs that appear plausible, yet are hard to classify, hence causing the model to rely on the (easier-to-learn) backdoor trigger.

Insights on "Label-Consistent Backdoor Attacks"

Deep neural networks (DNNs) are notably vulnerable to backdoor attacks, which are a form of adversarial manipulation where maliciously crafted inputs are injected into a model’s training dataset. Such attacks can later be triggered during inference to manipulate the model's behavior based on the adversary's objectives. The paper "Label-Consistent Backdoor Attacks" addresses the primary limitation of conventional backdoor attacks: the injected inputs are usually conspicuously mislabeled, and hence can be detected upon human inspection. By proposing a methodology to achieve label-consistent backdoor attacks, the authors significantly strengthen the stealth of backdoor attacks, making them much more challenging to detect.

Contributions and Methodology

This paper's central contribution lies in demonstrating the feasibility of label-consistent backdoor attacks. It pivots on the idea of using inputs that, while difficult to classify, maintain consistency with their given labels, thus avoiding detection during inspection. The authors propose innovative techniques leveraging both adversarial perturbations and generative models.

1. Adversarial Perturbations: By slightly perturbing natural inputs using adversarial techniques, it becomes difficult for a model to classify these inputs without the backdoor trigger. The use of Projected Gradient Descent (PGD) ensures these perturbations are effectively optimized, thus subtly altering the original inputs without making them appear mislabeled.
2. Latent Space Interpolation Using GANs: The paper innovatively uses Generative Adversarial Networks (GANs) to interpolate between inputs in the latent space. By finding embeddings in the GAN's latent space and interpolating towards incorrect classes, they produce inputs that are computationally challenging to classify, yet label-consistent upon visual inspection.
3. Backdoor Trigger Design Improvement: They enhance the backdoor trigger's design by reducing its visibility and making it robust to data augmentations common in ML training pipelines. This configuration leverages a multi-corner backdoor trigger approach that remains visible under transformations like flips and crops.

Strong Numerical Results and Evaluations

The experiments conducted across the CIFAR-10 dataset demonstrate the strength of these methods. Compared to the conventional backdoor approaches, techniques proposed in this paper achieve a substantially higher attack success rate with minimal inputs injected, following thorough evaluation under various settings. For example, adversarial perturbations model achieved more than a 50% attack success rate on multiple classes by injecting only about 75 samples while maintaining label consistency, which is a significant performance leap over the naive label-consistent baseline that failed to perform effectively.

Moreover, the paper provides quantitative insights into how training loss behaves for poisoned samples, validating that these samples remain difficult to classify throughout the model's training until the backdoor is leveraged. This empirical insight further supports the hypothesis and showcases the practical effectiveness of the proposed attacks.

Implications and Future Developments

The implications of these findings are profound; demonstrating viable label-consistent backdoor attacks underscores the crucial limitations in current datasets' security protocols and strategies aimed at preventing adversarial manipulations. The research suggests extending this groundwork to further refine attack methodologies and explore broader classes of data distributions and more complex model architectures.

Looking forward, potential defenses against such sophisticated attacks will require equally sophisticated countermeasures, likely focused on the early detection of subtle anomalies in the model's decision-making pathways and more robust filtering methodologies before training. Additionally, more exploring hybrid models that integrate cross-architecture learning transfer, persistent through adversarial challenges, will be necessary.

Conclusion

The presentation of label-consistent backdoor attacks in this paper brings to light a critical vulnerability in DNN training, urging the academic and broader AI community to reassess existing defenses. Since these attacks seamlessly evade detection, focus should now be keenly oriented on developing principled and theoretically sound defensive approaches capable of thwarting the insidious adversarial strategies delineated herein. The methodologies and insights from this paper are pivotal stepping stones towards the advancement of secure AI systems in adversarial settings.