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Register a new userKryptonite: An Adversarial Attack Using Regional Focus
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Added by
Yogesh Kulkarni
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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With the Rise of Adversarial Machine Learning and increasingly robust adversarial attacks, the security of applications utilizing the power of Machine Learning has been questioned. Over the past few years, applications of Deep Learning using Deep Neural Networks(DNN) in several fields including Medical Diagnosis, Security Systems, Virtual Assistants, etc. have become extremely commonplace, and hence become more exposed and susceptible to attack. In this paper, we present a novel study analyzing the weaknesses in the security of deep learning systems. We propose Kryptonite, an adversarial attack on images. We explicitly extract the Region of Interest (RoI) for the images and use it to add imperceptible adversarial perturbations to images to fool the DNN. We test our attack on several DNNs and compare our results with state of the art adversarial attacks like Fast Gradient Sign Method (FGSM), DeepFool (DF), Momentum Iterative Fast Gradient Sign Method (MIFGSM), and Projected Gradient Descent (PGD). The results obtained by us cause a maximum drop in network accuracy while yielding minimum possible perturbation and in considerably less amount of time per sample. We thoroughly evaluate our attack against three adversarial defence techniques and the promising results showcase the efficacy of our attack.
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As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to adversarial attacks. Though various defense mechanisms have been proposed to improve robustness of deep learning software, many of them are ineffective against adaptive attacks. In this work, we propose a novel characterization to distinguish adversarial examples from benign ones based on the observation that adversarial examples are significantly less robust than benign ones. As existing robustness measurement does not scale to large networks, we propose a novel defense framework, named attack as defense (A2D), to detect adversarial examples by effectively evaluating an examples robustness. A2D uses the cost of attacking an input for robustness evaluation and identifies those less robust examples as adversarial since less robust examples are easier to attack. Extensive experiment results on MNIST, CIFAR10 and ImageNet show that A2D is more effective than recent promising approaches. We also evaluate our defence against potential adaptive attacks and show that A2D is effective in defending carefully designed adaptive attacks, e.g., the attack success rate drops to 0% on CIFAR10.
The paper develops a new adversarial attack against deep neural networks (DNN), based on applying bio-inspired design to moving physical objects. To the best of our knowledge, this is the first work to introduce physical attacks with a moving object. Instead of following the dominating attack strategy in the existing literature, i.e., to introduce minor perturbations to a digital input or a stationary physical object, we show two new successful attack strategies in this paper. We show by superimposing several patterns onto one physical object, a DNN becomes confused and picks one of the patterns to assign a class label. Our experiment with three flapping wing robots demonstrates the possibility of developing an adversarial camouflage to cause a targeted mistake by DNN. We also show certain motion can reduce the dependency among consecutive frames in a video and make an object detector blind, i.e., not able to detect an object exists in the video. Hence in a successful physical attack against DNN, targeted motion against the system should also be considered.
Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods. Current successful attack methods for texts usually adopt heuristic replacement strategies on the character or word level, which remains challenging to find the optimal solution in the massive space of possible combinations of replacements while preserving semantic consistency and language fluency. In this paper, we propose textbf{BERT-Attack}, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT. We turn BERT against its fine-tuned models and other deep neural models in downstream tasks so that we can successfully mislead the target models to predict incorrectly. Our method outperforms state-of-the-art attack strategies in both success rate and perturb percentage, while the generated adversarial samples are fluent and semantically preserved. Also, the cost of calculation is low, thus possible for large-scale generations. The code is available at https://github.com/LinyangLee/BERT-Attack.
The visualization of future generation Wireless Communication Network WCN redirects the presumption of onward innovations, the fulfillment of user demands in the form of high data rates, energy efficiency, low latency, and long-range services. To content these demands, various technologies such as massive MIMO Multiple Input Multiple Output, UDN Ultra Dense Network, spectrum sharing, D2D Device to Device communication were improvised in the next generation WCN. In comparison to previous technologies, these technologies exhibit flat architecture, the involvement of clouds in the network, centralized architecture incorporating small cells which creates vulnerable breaches initiating menaces to the security of the network. The half-duplex attack is another threat to the WCN, where the resource spoofing mechanism is attained in the downlink phase of D2D communication. Instead of triggering an attack on both uplink and downlink, solely downlink is targeted by the attacker. This scheme allows the reduced failed attempt rate of the attacker as compared to the conventional attacks. The analysis is determined on the basis of Poissons distribution to determine the probability of failed attempts of half duplex attack in contrast to a full duplex attack
In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.
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