The Translucent Patch: A Physical and Universal Attack on Object Detectors

Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we propose a contactless translucent physical patch containing a carefully constructed pattern, which is placed on the camera's lens, to fool state-of-the-art object detectors. The primary goal of our patch is to hide all instances of a selected target class. In addition, the optimization method used to construct the patch aims to ensure that the detection of other (untargeted) classes remains unharmed. Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes. We show that our patch was able to prevent the detection of 42.27% of all stop sign instances while maintaining high (nearly 80%) detection of the other classes.

Detection of Adversarial Supports in Few-shot Classifiers Using Feature Preserving Autoencoders and Self-Similarity

Few-shot classifiers excel under limited training samples, making it useful in real world applications. However, the advent of adversarial samples threatens the efficacy of such classifiers. For them to remain reliable, defences against such attacks must be explored. However, closer examination to prior literature reveals a big gap in this domain. Hence, in this work, we propose a detection strategy to highlight adversarial support sets, aiming to destroy a few-shot classifier's understanding of a certain class of objects. We make use of feature preserving autoencoder filtering and also the concept of self-similarity of a support set to perform this detection. As such, our method is attack-agnostic and also the first to explore detection for few-shot classifiers to the best of our knowledge. Our evaluation on the miniImagenet and CUB datasets exhibit optimism when employing our proposed approach, showing high AUROC scores for detection in general.

Deja vu from the SVM Era: Example-based Explanations with Outlier Detection

Understanding the features that contributed to a prediction is important for high-stake tasks. In this work, we revisit the idea of a student network to provide an example-based explanation for its prediction in two forms: i) identify top-k most relevant prototype examples and ii) show evidence of similarity between the prediction sample and each of the top-k prototypes. We compare the prediction performance and the explanation performance for the second type of explanation with the teacher network. In addition, we evaluate the outlier detection performance of the network. We show that using prototype-based students beyond similarity kernels deliver meaningful explanations and promising outlier detection results, without compromising on classification accuracy.

Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers

The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training set, there is a need to continuously collect benign and malicious files from a wide range of sources, providing an exploitable target to attackers. In this study, we show how an attacker can launch a sophisticated and efficient poisoning attack targeting the dataset used to train a malware classifier. The attacker's ultimate goal is to ensure that the model induced by the poisoned dataset will be unable to detect the attacker's malware yet capable of detecting other malware. As opposed to other poisoning attacks in the malware detection domain, our attack does not focus on malware families but rather on specific malware instances that contain an implanted trigger, reducing the detection rate from 99.23% to 0% depending on the amount of poisoning. We evaluate our attack on the EMBER dataset with a state-of-the-art classifier and malware samples from VirusTotal for end-to-end validation of our work. We propose a comprehensive detection approach that could serve as a future sophisticated defense against this newly discovered severe threat.

Dynamic Adversarial Patch for Evading Object Detection Models

Recent research shows that neural networks models used for computer vision (e.g., YOLO and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real-world adversarial attacks against object detectors use an adversarial patch which is attached to the target object (e.g., a carefully crafted sticker placed on a stop sign). This method may not be robust to changes in the camera's location relative to the target object; in addition, it may not work well when applied to nonplanar objects such as cars. In this study, we present an innovative attack method against object detectors applied in a real-world setup that addresses some of the limitations of existing attacks. Our method uses dynamic adversarial patches which are placed at multiple predetermined locations on a target object. An adversarial learning algorithm is applied in order to generate the patches used. The dynamic attack is implemented by switching between optimized patches dynamically, according to the camera's position (i.e., the object detection system's position). In order to demonstrate our attack in a real-world setup, we implemented the patches by attaching flat screens to the target object; the screens are used to present the patches and switch between them, depending on the current camera location. Thus, the attack is dynamic and adjusts itself to the situation to achieve optimal results. We evaluated our dynamic patch approach by attacking the YOLOv2 object detector with a car as the target object and succeeded in misleading it in up to 90% of the video frames when filming the car from a wide viewing angle range. We improved the attack by generating patches that consider the semantic distance between the target object and its classification. We also examined the attack's transferability among different car models and were able to mislead the detector 71% of the time.

Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations

Mass surveillance systems for voice over IP (VoIP) conversations pose a huge risk to privacy. These automated systems use learning models to analyze conversations, and upon detecting calls that involve specific topics, route them to a human agent. In this study, we present an adversarial learning-based framework for privacy protection for VoIP conversations. We present a novel algorithm that finds a universal adversarial perturbation (UAP), which, when added to the audio stream, prevents an eavesdropper from automatically detecting the conversation's topic. As shown in our experiments, the UAP is agnostic to the speaker or audio length, and its volume can be changed in real-time, as needed. In a real-world demonstration, we use a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real-time. We examine different speakers, VoIP applications (Skype, Zoom), audio lengths, and speech-to-text models (Deep Speech, Kaldi). Our results in the real world suggest that our approach is a feasible solution for privacy protection.

When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders

In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize learning models to predict the market's behavior and execute an investment strategy accordingly. However, learning models have been shown to be susceptible to input manipulations called adversarial examples. Yet, the trading domain remains largely unexplored in the context of adversarial learning. This is mainly because of the rapid changes in the market which impair the attacker's ability to create a real-time attack. In this study, we present a realistic scenario in which an attacker gains control of an algorithmic trading bots by manipulating the input data stream in real-time. The attacker creates an universal perturbation that is agnostic to the target model and time of use, while also remaining imperceptible. We evaluate our attack on a real-world market data stream and target three different trading architectures. We show that our perturbation can fool the model at future unseen data points, in both white-box and black-box settings. We believe these findings should serve as an alert to the finance community about the threats in this area and prompt further research on the risks associated with using automated learning models in the finance domain.

Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples

Recent work on adversarial learning has focused mainly on neural networks and domains where they excel, such as computer vision. The data in these domains is homogeneous, whereas heterogeneous tabular data domains remain underexplored despite their prevalence. Constructing an attack on models with heterogeneous input spaces is challenging, as they are governed by complex domain-specific validity rules and comprised of nominal, ordinal, and numerical features. We argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images. In this paper, we introduce an optimization framework for identifying adversarial perturbations in heterogeneous input spaces. We define distribution-aware constraints for preserving the consistency of the adversarial examples and incorporate them by embedding the heterogeneous input into a continuous latent space. Our approach focuses on an adversary who aims to craft valid perturbations of minimal l_0-norms and apply them in real life. We propose a neural network-based implementation of our approach and demonstrate its effectiveness using three datasets from different content domains. Our results suggest that despite the several constraints heterogeneity imposes on the input space of a machine learning model, the susceptibility to adversarial examples remains unimpaired.

Fairness Matters -- A Data-Driven Framework Towards Fair and High Performing Facial Recognition Systems

Facial recognition technologies are widely used in governmental and industrial applications. Together with the advancements in deep learning (DL), human-centric tasks such as accurate age prediction based on face images become feasible. However, the issue of fairness when predicting the age for different ethnicity and gender remains an open problem. Policing systems use age to estimate the likelihood of someone to commit a crime, where younger suspects tend to be more likely involved. Unfair age prediction may lead to unfair treatment of humans not only in crime prevention but also in marketing, identity acquisition and authentication. Therefore, this work follows two parts. First, an empirical study is conducted evaluating performance and fairness of state-of-the-art systems for age prediction including baseline and most recent works of academia and the main industrial service providers (Amazon AWS and Microsoft Azure). Building on the findings we present a novel approach to mitigate unfairness and enhance performance, using distribution-aware dataset curation and augmentation. Distribution-awareness is based on out-of-distribution detection which is utilized to validate equal and diverse DL system behavior towards e.g. ethnicity and gender. In total we train 24 DNN models and utilize one million data points to assess performance and fairness of the state-of-the-art for face recognition algorithms. We demonstrate an improvement in mean absolute age prediction error from 7.70 to 3.39 years and a 4-fold increase in fairness towards ethnicity when compared to related work. Utilizing the presented methodology we are able to outperform leading industry players such as Amazon AWS or Microsoft Azure in both fairness and age prediction accuracy and provide the necessary guidelines to assess quality and enhance face recognition systems based on DL techniques.