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Register a new userA Novel Data Encryption Method Inspired by Adversarial Attacks
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Added by
Praveen Fernando
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Due to the advances of sensing and storage technologies, a tremendous amount of data becomes available and, it supports the phenomenal growth of artificial intelligence (AI) techniques especially, deep learning (DL), in various application domains. While the data sources become valuable assets for enabling the success of autonomous decision-making, they also lead to critical vulnerabilities in privacy and security. For example, data leakage can be exploited via querying and eavesdropping in the exploratory phase for black-box attacks against DL-based autonomous decision-making systems. To address this issue, in this work, we propose a novel data encryption method, called AdvEncryption, by exploiting the principle of adversarial attacks. Different from existing encryption technologies, the AdvEncryption method is not developed to prevent attackers from exploiting the dataset. Instead, our proposed method aims to trap the attackers in a misleading feature distillation of the data. To achieve this goal, our AdvEncryption method consists of two essential components: 1) an adversarial attack-inspired encryption mechanism to encrypt the data with stealthy adversarial perturbation, and 2) a decryption mechanism that minimizes the impact of the perturbations on the effectiveness of autonomous decision making. In the performance evaluation section, we evaluate the performance of our proposed AdvEncryption method through case studies considering different scenarios.
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Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model security. In this paper, a new procedure called Composite Adversarial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms and their hyper-parameters from a candidate pool of textbf{32 base attackers}. We design a search space where attack policy is represented as an attacking sequence, i.e., the output of the previous attacker is used as the initialization input for successors. Multi-objective NSGA-II genetic algorithm is adopted for finding the strongest attack policy with minimum complexity. The experimental result shows CAA beats 10 top attackers on 11 diverse defenses with less elapsed time (textbf{6 $times$ faster than AutoAttack}), and achieves the new state-of-the-art on $l_{infty}$, $l_{2}$ and unrestricted adversarial attacks.
Recently, the membership inference attack poses a serious threat to the privacy of confidential training data of machine learning models. This paper proposes a novel adversarial example based privacy-preserving technique (AEPPT), which adds the crafted adversarial perturbations to the prediction of the target model to mislead the adversarys membership inference model. The added adversarial perturbations do not affect the accuracy of target model, but can prevent the adversary from inferring whether a specific data is in the training set of the target model. Since AEPPT only modifies the original output of the target model, the proposed method is general and does not require modifying or retraining the target model. Experimental results show that the proposed method can reduce the inference accuracy and precision of the membership inference model to 50%, which is close to a random guess. Further, for those adaptive attacks where the adversary knows the defense mechanism, the proposed AEPPT is also demonstrated to be effective. Compared with the state-of-the-art defense methods, the proposed defense can significantly degrade the accuracy and precision of membership inference attacks to 50% (i.e., the same as a random guess) while the performance and utility of the target model will not be affected.
Today, two-factor authentication (2FA) is a widely implemented mechanism to counter phishing attacks. Although much effort has been investigated in 2FA, most 2FA systems are still vulnerable to carefully designed phishing attacks, and some even request special hardware, which limits their wide deployment. Recently, real-time phishing (RTP) has made the situation even worse because an adversary can effortlessly establish a phishing website replicating a target website without any background of the web page design technique. Traditional 2FA can be easily bypassed by such RTP attacks. In this work, we propose a novel 2FA system to counter RTP attacks. The main idea is to request a user to take a photo of the web browser with the domain name in the address bar as the 2nd authentication factor. The web server side extracts the domain name information based on Optical Character Recognition (OCR), and then determines if the user is visiting this website or a fake one, thus defeating the RTP attacks where an adversary must set up a fake website with a different domain. We prototyped our system and evaluated its performance in various environments. The results showed that PhotoAuth is an effective technique with good scalability. We also showed that compared to other 2FA systems, PhotoAuth has several advantages, especially no special hardware or software support is needed on the client side except a phone, making it readily deployable.
Being an emerging class of in-memory computing architecture, brain-inspired hyperdimensional computing (HDC) mimics brain cognition and leverages random hypervectors (i.e., vectors with a dimensionality of thousands or even more) to represent features and to perform classification tasks. The unique hypervector representation enables HDC classifiers to exhibit high energy efficiency, low inference latency and strong robustness against hardware-induced bit errors. Consequently, they have been increasingly recognized as an appealing alternative to or even replacement of traditional deep neural networks (DNNs) for local on device classification, especially on low-power Internet of Things devices. Nonetheless, unlike their DNN counterparts, state-of-the-art designs for HDC classifiers are mostly security-oblivious, casting doubt on their safety and immunity to adversarial inputs. In this paper, we study for the first time adversarial attacks on HDC classifiers and highlight that HDC classifiers can be vulnerable to even minimally-perturbed adversarial samples. Concretely, using handwritten digit classification as an example, we construct a HDC classifier and formulate a grey-box attack problem, where an attackers goal is to mislead the target HDC classifier to produce erroneous prediction labels while keeping the amount of added perturbation noise as little as possible. Then, we propose a modified genetic algorithm to generate adversarial samples within a reasonably small number of queries. Our results show that adversarial images generated by our algorithm can successfully mislead the HDC classifier to produce wrong prediction labels with a high probability (i.e., 78% when the HDC classifier uses a fixed majority rule for decision). Finally, we also present two defense strategies -- adversarial training and retraining-- to strengthen the security of HDC classifiers.
A class of data integrity attack, known as false data injection (FDI) attack, has been studied with a considerable amount of work. It has shown that with perfect knowledge of the system model and the capability to manipulate a certain number of measurements, the FDI attacks can coordinate measurements corruption to keep stealth against the bad data detection. However, a more realistic attack is essentially an attack with limited adversarial knowledge of the system model and limited attack resources due to various reasons. In this paper, we generalize the data attacks that they can be pure FDI attacks or combined with availability attacks (e.g., DoS attacks) and analyze the attacks with limited adversarial knowledge or limited attack resources. The attack impact is evaluated by the proposed metrics and the detection probability of attacks is calculated using the distribution property of data with or without attacks. The analysis is supported with results from a power system use case. The results show how important the knowledge is to the attacker and which measurements are more vulnerable to attacks with limited resources.
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