Deep neural networks (DNNs) are widely used to handle many difficult tasks, such as image classification and malware detection, and achieve outstanding performance. However, recent studies on adversarial examples, which have maliciously undetectable perturbations added to their original samples that are indistinguishable by human eyes but mislead the machine learning approaches, show that machine learning models are vulnerable to security attacks. Though various adversarial retraining techniques have been developed in the past few years, none of them is scalable. In this paper, we propose a new iterative adversarial retraining approach to robustify the model and to reduce the effectiveness of adversarial inputs on DNN models. The proposed method retrains the model with both Gaussian noise augmentation and adversarial generation techniques for better generalization. Furthermore, the ensemble model is utilized during the testing phase in order to increase the robust test accuracy. The results from our extensive experiments demonstrate that the proposed approach increases the robustness of the DNN model against various adversarial attacks, specifically, fast gradient sign attack, Carlini and Wagner (C&W) attack, Projected Gradient Descent (PGD) attack, and DeepFool attack. To be precise, the robust classifier obtained by our proposed approach can maintain a performance accuracy of 99% on average on the standard test set. Moreover, we empirically evaluate the runtime of two of the most effective adversarial attacks, i.e., C&W attack and BIM attack, to find that the C&W attack can utilize GPU for faster adversarial example generation than the BIM attack can. For this reason, we further develop a parallel implementation of the proposed approach. This parallel implementation makes the proposed approach scalable for large datasets and complex models.
Robust Ensemble Morph Detection with Domain Generalization
Although a substantial amount of studies is dedicated to
morphing detection, most of them fail to generalize for morph
faces outside of their training paradigm. Moreover, recent
morph detection methods are highly vulnerable to adversarial
attacks. In this paper, we intend to learn a morph
detection model with high generalization to a wide range
of morphing attacks and high robustness against different
adversarial attacks. To this aim, we develop an ensemble
of convolutional neural networks (CNNs) and Transformer
models to benefit from their capabilities simultaneously. To
improve the robust accuracy of the ensemble model, we employ
multi-perturbation adversarial training and generate
adversarial examples with high transferability for several
single models. Our exhaustive evaluations demonstrate that
the proposed robust ensemble model generalizes to several
morphing attacks and face datasets. In addition, we validate
that our robust ensemble model gains better robustness
against several adversarial attacks while outperforming the
state-of-the-art studies.
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- Award ID(s):
- 1650474
- NSF-PAR ID:
- 10401293
- Date Published:
- Journal Name:
- 2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such models are susceptible to adversarial evasion attacks. In this type of attack, inputs (i.e., adversarial examples) are specially crafted by intelligent malicious adversaries, with the aim of being misclassified by existing state-of-the-art models (e.g., deep neural networks). Once the attackers can fool a classifier to think that a malicious input is actually benign, they can render a machine learning-based malware or intrusion detection system ineffective. Objective To help security practitioners and researchers build a more robust model against non-adaptive, white-box and non-targeted adversarial evasion attacks through the idea of ensemble model. Method We propose an approach called Omni, the main idea of which is to explore methods that create an ensemble of “unexpected models”; i.e., models whose control hyperparameters have a large distance to the hyperparameters of an adversary’s target model, with which we then make an optimized weighted ensemble prediction. Results In studies with five types of adversarial evasion attacks (FGSM, BIM, JSMA, DeepFool and Carlini-Wagner) on five security datasets (NSL-KDD, CIC-IDS-2017, CSE-CIC-IDS2018, CICAndMal2017 and the Contagio PDF dataset), we show Omni is a promising approach as a defense strategy against adversarial attacks when compared with other baseline treatments Conclusions When employing ensemble defense against adversarial evasion attacks, we suggest to create ensemble with unexpected models that are distant from the attacker’s expected model (i.e., target model) through methods such as hyperparameter optimization.more » « less
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Deep Neural Networks (DNNs) need to be both efficient and robust for practical uses. Quantization and structure simplification are promising ways to adapt DNNs to mobile devices, and adversarial training is one of the most successful methods to train robust DNNs. In this work, we aim to realize both advantages by applying a convergent relaxation quantization algorithm, i.e., Binary-Relax (BR), to an adversarially trained robust model, i.e. the ResNets Ensemble via Feynman-Kac Formalism (EnResNet). We discover that high-precision quantization, such as ternary (tnn) or 4-bit, produces sparse DNNs. However, this sparsity is unstructured under adversarial training. To solve the problems that adversarial training jeopardizes DNNs’ accuracy on clean images and break the structure of sparsity, we design a trade-off loss function that helps DNNs preserve natural accuracy and improve channel sparsity. With our newly designed trade-off loss function, we achieve both goals with no reduction of resistance under weak attacks and very minor reduction of resistance under strong adversarial attacks. Together with our model and algorithm selections and loss function design, we provide an integrated approach to produce robust DNNs with high efficiency and accuracy. Furthermore, we provide a missing benchmark on robustness of quantized models.more » « less
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Models produced by machine learning, particularly deep neural networks, are state-of-the-art for many machine learning tasks and demonstrate very high prediction accuracy. Unfortunately, these models are also very brittle and vulnerable to specially crafted adversarial examples. Recent results have shown that accuracy of these models can be reduced from close to hundred percent to below 5\% using adversarial examples. This brittleness of deep neural networks makes it challenging to deploy these learning models in security-critical areas where adversarial activity is expected, and cannot be ignored. A number of methods have been recently proposed to craft more effective and generalizable attacks on neural networks along with competing efforts to improve robustness of these learning models. But the current approaches to make machine learning techniques more resilient fall short of their goal. Further, the succession of new adversarial attacks against proposed methods to increase neural network robustness raises doubts about a foolproof approach to robustify machine learning models against all possible adversarial attacks. In this paper, we consider the problem of detecting adversarial examples. This would help identify when the learning models cannot be trusted without attempting to repair the models or make them robust to adversarial attacks. This goal of finding limitations of the learning model presents a more tractable approach to protecting against adversarial attacks. Our approach is based on identifying a low dimensional manifold in which the training samples lie, and then using the distance of a new observation from this manifold to identify whether this data point is adversarial or not. Our empirical study demonstrates that adversarial examples not only lie farther away from the data manifold, but this distance from manifold of the adversarial examples increases with the attack confidence. Thus, adversarial examples that are likely to result into incorrect prediction by the machine learning model is also easier to detect by our approach. This is a first step towards formulating a novel approach based on computational geometry that can identify the limiting boundaries of a machine learning model, and detect adversarial attacks.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IJCB54206.2022.10007929
