Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRobustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps
204
0
0.0
(
0
)
Added by
Yujin Huang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition. To make it more accessible to end users, many deep learning models are now embedded in mobile apps. Compared to offloading deep learning from smartphones to the cloud, performing machine learning on-device can help improve latency, connectivity, and power consumption. However, most deep learning models within Android apps can easily be obtained via mature reverse engineering, while the models exposure may invite adversarial attacks. In this study, we propose a simple but effective approach to hacking deep learning models using adversarial attacks by identifying highly similar pre-trained models from TensorFlow Hub. All 10 real-world Android apps in the experiment are successfully attacked by our approach. Apart from the feasibility of the model attack, we also carry out an empirical study that investigates the characteristics of deep learning models used by hundreds of Android apps on Google Play. The results show that many of them are similar to each other and widely use fine-tuning techniques to pre-trained models on the Internet.
rate research
Read More
With the success of the graph embedding model in both academic and industry areas, the robustness of graph embedding against adversarial attack inevitably becomes a crucial problem in graph learning. Existing works usually perform the attack in a white-box fashion: they need to access the predictions/labels to construct their adversarial loss. However, the inaccessibility of predictions/labels makes the white-box attack impractical to a real graph learning system. This paper promotes current frameworks in a more general and flexible sense -- we demand to attack various kinds of graph embedding models with black-box driven. We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter. Therefore, we design a generalized adversarial attacker: GF-Attack. Without accessing any labels and model predictions, GF-Attack can perform the attack directly on the graph filter in a black-box fashion. We further prove that GF-Attack can perform an effective attack without knowing the number of layers of graph embedding models. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experiments validate the effectiveness of GF-Attack on several benchmark datasets.
While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of fake components, e.g., permissions and system calls, without compromising intrusive functionality. Previous work has shown that, to improve robustness against such attacks, learning algorithms should avoid overemphasizing few discriminant features, providing instead decisions that rely upon a large subset of components. In this work, we investigate whether gradient-based attribution methods, used to explain classifiers decisions by identifying the most relevant features, can be used to help identify and select more robust algorithms. To this end, we propose to exploit two different metrics that represent the evenness of explanations, and a new compact security measure called Adversarial Robustness Metric. Our experiments conducted on two different datasets and five classification algorithms for Android malware detection show that a strong connection exists between the uniformity of explanations and adversarial robustness. In particular, we found that popular techniques like Gradient*Input and Integrated Gradients are strongly correlated to security when applied to both linear and nonlinear detectors, while more elementary explanation techniques like the simple Gradient do not provide reliable information about the robustness of such classifiers.
Deep learning models are increasingly used in mobile applications as critical components. Unlike the program bytecode whose vulnerabilities and threats have been widely-discussed, whether and how the deep learning models deployed in the applications can be compromised are not well-understood since neural networks are usually viewed as a black box. In this paper, we introduce a highly practical backdoor attack achieved with a set of reverse-engineering techniques over compiled deep learning models. The core of the attack is a neural conditional branch constructed with a trigger detector and several operators and injected into the victim model as a malicious payload. The attack is effective as the conditional logic can be flexibly customized by the attacker, and scalable as it does not require any prior knowledge from the original model. We evaluated the attack effectiveness using 5 state-of-the-art deep learning models and real-world samples collected from 30 users. The results demonstrated that the injected backdoor can be triggered with a success rate of 93.5%, while only brought less than 2ms latency overhead and no more than 1.4% accuracy decrease. We further conducted an empirical study on real-world mobile deep learning apps collected from Google Play. We found 54 apps that were vulnerable to our attack, including popular and security-critical ones. The results call for the awareness of deep learning application developers and auditors to enhance the protection of deployed models.
Deep learning-based time series models are being extensively utilized in engineering and manufacturing industries for process control and optimization, asset monitoring, diagnostic and predictive maintenance. These models have shown great improvement in the prediction of the remaining useful life (RUL) of industrial equipment but suffer from inherent vulnerability to adversarial attacks. These attacks can be easily exploited and can lead to catastrophic failure of critical industrial equipment. In general, different adversarial perturbations are computed for each instance of the input data. This is, however, difficult for the attacker to achieve in real time due to higher computational requirement and lack of uninterrupted access to the input data. Hence, we present the concept of universal adversarial perturbation, a special imperceptible noise to fool regression based RUL prediction models. Attackers can easily utilize universal adversarial perturbations for real-time attack since continuous access to input data and repetitive computation of adversarial perturbations are not a prerequisite for the same. We evaluate the effect of universal adversarial attacks using NASA turbofan engine dataset. We show that addition of universal adversarial perturbation to any instance of the input data increases error in the output predicted by the model. To the best of our knowledge, we are the first to study the effect of the universal adversarial perturbation on time series regression models. We further demonstrate the effect of varying the strength of perturbations on RUL prediction models and found that model accuracy decreases with the increase in perturbation strength of the universal adversarial attack. We also showcase that universal adversarial perturbation can be transferred across different models.
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defenses for graph data. In this paper, we propose both attack and defense techniques. For attack, we show that the discreteness problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defense, we observe that the adversarially manipulated graph for the targeted attack differs from normal graphs statistically. Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations. Our experiments on a number of datasets show the effectiveness of the proposed methods.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
