SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency

02/07/2023
by   Junfeng Guo, et al.
0

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation. These attacks pose great threats to the applications of DNNs under the real-world machine learning as a service (MLaaS) setting, where the deployed model is fully black-box while the users can only query and obtain its predictions. Currently, there are many existing defenses to reduce backdoor threats. However, almost all of them cannot be adopted in MLaaS scenarios since they require getting access to or even modifying the suspicious models. In this paper, we propose a simple yet effective black-box input-level backdoor detection, called SCALE-UP, which requires only the predicted labels to alleviate this problem. Specifically, we identify and filter malicious testing samples by analyzing their prediction consistency during the pixel-wise amplification process. Our defense is motivated by an intriguing observation (dubbed scaled prediction consistency) that the predictions of poisoned samples are significantly more consistent compared to those of benign ones when amplifying all pixel values. Besides, we also provide theoretical foundations to explain this phenomenon. Extensive experiments are conducted on benchmark datasets, verifying the effectiveness and efficiency of our defense and its resistance to potential adaptive attacks. Our codes are available at https://github.com/JunfengGo/SCALE-UP.

READ FULL TEXT
research
03/24/2021

Black-box Detection of Backdoor Attacks with Limited Information and Data

Although deep neural networks (DNNs) have made rapid progress in recent ...
research
08/04/2022

MOVE: Effective and Harmless Ownership Verification via Embedded External Features

Currently, deep neural networks (DNNs) are widely adopted in different a...
research
11/19/2021

Enhanced countering adversarial attacks via input denoising and feature restoring

Despite the fact that deep neural networks (DNNs) have achieved prominen...
research
07/17/2023

Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of Sound

Deep neural networks (DNNs) have been widely and successfully adopted an...
research
04/08/2022

An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks

With the surge of Machine Learning (ML), An emerging amount of intellige...
research
03/27/2023

Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder

Deep neural networks are vulnerable to backdoor attacks, where an advers...
research
11/02/2022

Backdoor Defense via Suppressing Model Shortcuts

Recent studies have demonstrated that deep neural networks (DNNs) are vu...

Please sign up or login with your details

Forgot password? Click here to reset