arXiv subjects

Amira Guesmi

Publications and source records attributed to Amira Guesmi.

26 records · Page 2Linked to original sources

arXiv2023

AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems

Vision-based perception modules are increasingly deployed in many applications, especially autonomous vehicles and intelligent robots. These modules are being used to acquire information about the surroundings and identify obstacles. Hence, accurate detection and classification are essential to reach appropriate decisions and take appropriate and safe actions at all times. Current studies have demonstrated that "printed adversarial attacks", known as physical adversarial attacks, can successfully mislead perception models such as object detectors and image classifiers. However, most of these physical attacks are based on noticeable and eye-catching patterns for generated perturbations making them identifiable/detectable by human eye or in test drives. In this paper, we propose a camera-based inconspicuous adversarial attack (\textbf{AdvRain}) capable of fooling camera-based perception systems over all objects of the same class. Unlike mask based fake-weather attacks that require access to the underlying computing hardware or image memory, our attack is based on emulating the effects of a natural weather condition (i.e., Raindrops) that can be printed on a translucent sticker, which is externally placed over the lens of a camera. To accomplish this, we provide an iterative process based on performing a random search aiming to identify critical positions to make sure that the performed transformation is adversarial for a target classifier. Our transformation is based on blurring predefined parts of the captured image corresponding to the areas covered by the raindrop. We achieve a drop in average model accuracy of more than $45\%$ and $40\%$ on VGG19 for ImageNet and Resnet34 for Caltech-101, respectively, using only $20$ raindrops.

cs.CV↗

arXiv2023

Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Furthermore, we explore the specific requirements and challenges associated with executing attacks in the physical world. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications, including classification, detection, face recognition, semantic segmentation and depth estimation. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness. We examine how each technique strives to ensure the successful manipulation of DNNs while mitigating the risk of detection and withstanding real-world distortions. Lastly, we discuss the current challenges and outline potential future research directions in the field of physical adversarial attacks. We highlight the need for enhanced defense mechanisms, the exploration of novel attack strategies, the evaluation of attacks in different application domains, and the establishment of standardized benchmarks and evaluation criteria for physical adversarial attacks. Through this comprehensive survey, we aim to provide a valuable resource for researchers, practitioners, and policymakers to gain a holistic understanding of physical adversarial attacks in computer vision and facilitate the development of robust and secure DNN-based systems.

cs.CR↗

arXiv2023

Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens

Machine Learning (ML) architectures have been applied to several applications that involve sensitive data, where a guarantee of users' data privacy is required. Differentially Private Stochastic Gradient Descent (DPSGD) is the state-of-the-art method to train privacy-preserving models. However, DPSGD comes at a considerable accuracy loss leading to sub-optimal privacy/utility trade-offs. Towards investigating new ground for better privacy-utility trade-off, this work questions; (i) if models' hyperparameters have any inherent impact on ML models' privacy-preserving properties, and (ii) if models' hyperparameters have any impact on the privacy/utility trade-off of differentially private models. We propose a comprehensive design space exploration of different hyperparameters such as the choice of activation functions, the learning rate and the use of batch normalization. Interestingly, we found that utility can be improved by using Bounded RELU as activation functions with the same privacy-preserving characteristics. With a drop-in replacement of the activation function, we achieve new state-of-the-art accuracy on MNIST (96.02\%), FashionMnist (84.76\%), and CIFAR-10 (44.42\%) without any modification of the learning procedure fundamentals of DPSGD.

cs.LG↗

arXiv2022

Adversarial Attack on Radar-based Environment Perception Systems

Due to their robustness to degraded capturing conditions, radars are widely used for environment perception, which is a critical task in applications like autonomous vehicles. More specifically, Ultra-Wide Band (UWB) radars are particularly efficient for short range settings as they carry rich information on the environment. Recent UWB-based systems rely on Machine Learning (ML) to exploit the rich signature of these sensors. However, ML classifiers are susceptible to adversarial examples, which are created from raw data to fool the classifier such that it assigns the input to the wrong class. These attacks represent a serious threat to systems integrity, especially for safety-critical applications. In this work, we present a new adversarial attack on UWB radars in which an adversary injects adversarial radio noise in the wireless channel to cause an obstacle recognition failure. First, based on signals collected in real-life environment, we show that conventional attacks fail to generate robust noise under realistic conditions. We propose a-RNA, i.e., Adversarial Radio Noise Attack to overcome these issues. Specifically, a-RNA generates an adversarial noise that is efficient without synchronization between the input signal and the noise. Moreover, a-RNA generated noise is, by-design, robust against pre-processing countermeasures such as filtering-based defenses. Moreover, in addition to the undetectability objective by limiting the noise magnitude budget, a-RNA is also efficient in the presence of sophisticated defenses in the spectral domain by introducing a frequency budget. We believe this work should alert about potentially critical implementations of adversarial attacks on radar systems that should be taken seriously.

cs.CR↗

arXiv2022

Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks

Machine-learning architectures, such as Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks: inputs crafted carefully to force the system output to a wrong label. Since machine-learning is being deployed in safety-critical and security-sensitive domains, such attacks may have catastrophic security and safety consequences. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine-learning classifiers. We show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has unrestricted access to the approximate classifier implementation: in this case, we show that substantially higher levels of adversarial noise are needed to produce adversarial examples. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5, Alexnet and VGG-11 CNNs considerably with up to 50% by-product saving in energy consumption due to the simpler nature of the approximate logic.

cs.CR↗

arXiv2022

Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems

The real-world use cases of Machine Learning (ML) have exploded over the past few years. However, the current computing infrastructure is insufficient to support all real-world applications and scenarios. Apart from high efficiency requirements, modern ML systems are expected to be highly reliable against hardware failures as well as secure against adversarial and IP stealing attacks. Privacy concerns are also becoming a first-order issue. This article summarizes the main challenges in agile development of efficient, reliable and secure ML systems, and then presents an outline of an agile design methodology to generate efficient, reliable and secure ML systems based on user-defined constraints and objectives.

cs.AR↗

arXiv2022

ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints

Advances in deep learning have enabled a wide range of promising applications. However, these systems are vulnerable to Adversarial Machine Learning (AML) attacks; adversarially crafted perturbations to their inputs could cause them to misclassify. Several state-of-the-art adversarial attacks have demonstrated that they can reliably fool classifiers making these attacks a significant threat. Adversarial attack generation algorithms focus primarily on creating successful examples while controlling the noise magnitude and distribution to make detection more difficult. The underlying assumption of these attacks is that the adversarial noise is generated offline, making their execution time a secondary consideration. However, recently, just-in-time adversarial attacks where an attacker opportunistically generates adversarial examples on the fly have been shown to be possible. This paper introduces a new problem: how do we generate adversarial noise under real-time constraints to support such real-time adversarial attacks? Understanding this problem improves our understanding of the threat these attacks pose to real-time systems and provides security evaluation benchmarks for future defenses. Therefore, we first conduct a run-time analysis of adversarial generation algorithms. Universal attacks produce a general attack offline, with no online overhead, and can be applied to any input; however, their success rate is limited because of their generality. In contrast, online algorithms, which work on a specific input, are computationally expensive, making them inappropriate for operation under time constraints. Thus, we propose ROOM, a novel Real-time Online-Offline attack construction Model where an offline component serves to warm up the online algorithm, making it possible to generate highly successful attacks under time constraints.

cs.CR↗

arXiv2021

Defensive Approximation: Securing CNNs using Approximate Computing

In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide range of real-life problems. However, these architectures are vulnerable to adversarial attacks. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine learning classifiers. We show that our approximate computing implementation achieves robustness across a wide range of attack scenarios. Specifically, for black-box and grey-box attack scenarios, we show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has access to the internal implementation of the approximate classifier. We explain some of the possible reasons for this robustness through analysis of the internal operation of the approximate implementation. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5 and an Alexnet CNNs by up to 99% and 87%, respectively for strong grey-box adversarial attacks along with up to 67% saving in energy consumption due to the simpler nature of the approximate logic. We also show that a white-box attack requires a remarkably higher noise budget to fool the approximate classifier, causing an average of 4db degradation of the PSNR of the input image relative to the images that succeed in fooling the exact classifier

cs.CR↗