Query: frequency adversarial detection

title: Unsupervised Pixel level Road Defect Detection via Adversarial Image to Frequency Transform
abstract: In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements in various studies on computer vision and deep learning. Although large scale and well annotated datasets enhance the performance of detecting road defects to some extent, it is still challengeable to derive a model which can perform reliably for various road conditions in practice, because it is intractable to construct a dataset considering diverse road conditions and defect patterns. To end this, we propose an unsupervised approach to detect road defects, using Adversarial Image to Frequency Transform (AIFT) AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not require annotations for road defects. We evaluate the efficiency of AIFT using GAPs384 dataset, Cracktree200 dataset, CRACK500 dataset, and CFD dataset. The experimental results demonstrate that the proposed approach detects various road detects, and it outperforms existing state of the art approaches.

title: ADVoIP: Adversarial Detection of Encrypted and Concealed VoIP
abstract: A network attacker wants to transmit Voice over IP (VoIP) traffic streams covertly. He tries to evade the detection system by manipulating the VoIP streams through padding, shifting, and splitting operations, so as to conceal them amidst the Internet traffic. A defender wants to detect the manipulated VoIP streams. Tackling this problem from an adversarial perspective, we provide two contributions: 1) we obtain a highly stylized representation of VoIP streams in terms of transmission frequency <inline formula> <tex math notation="LaTeX"\mathscr {F} /tex math>/inline formula> and packet length <inline formula> <tex math notation="LaTeX"\mathscr {L} /tex math>/inline formula> and characterize the <inline formula> <tex math notation="LaTeX"\mathscr {F}\mathscr {L} /tex math>/inline formula> region achievable by the attacker's transformation and 2) We formulate the VoIP detection game, and find both theoretical conditions and a practical algorithm to find the Nash equilibrium of the game. As a result, we are able to design an optimal (from the adversarial perspective) algorithm for VoIP detection, which is nicknamed as ADVoIP. Simulations over real network traces, and comparison with existing approaches, show the effectiveness of the proposed approach.

title: Automated Detection System for Adversarial Examples with High Frequency Noises Sieve
abstract: Deep neural networks are being applied in many tasks with encouraging results, and have often reached human level performance. However, deep neural networks are vulnerable to well designed input samples called adversarial examples. In particular, neural networks tend to misclassify adversarial examples that are imperceptible to humans. This paper introduces a new detection system that automatically detects adversarial examples on deep neural networks. Our proposed system can mostly distinguish adversarial samples and benign images in an end to end manner without human intervention. We exploit the important role of the frequency domain in adversarial samples and propose a method that detects malicious samples in observations. When evaluated on two standard benchmark datasets (MNIST and ImageNet) our method achieved an out detection rate of 99.7 100% in many settings.

title: Adversarial Detection of Concealed VoIP Traffic
abstract: network attacker wants to transmit VoIP traffic streams covertly. He tries to evade the detection system by manipulating the VoIP streams through padding and shifting operations, so as to conceal them amidst the Internet traffic. A defender (the detection system) wants to detect the manipulated VoIP streams. Tackling this problem from an adversarial perspective, we provide two contributions: i) we obtain a highly stylized representation of VoIP streams in terms of transmission frequency \mathcal{F} and payload \mathcal{L} and characterize the \mathcal{F} \mathcal{L} region achievable by the attacker's transformation; ii) we formulate the VoIP detection game, and find both theoretical conditions as well as a practical algorithm to find the Nash equilibrium of the game. Simulations over real network traces, and comparison with standard, adversary unaware tools, show the advantages of the adopted adversarial perspective.

title: Adversarial Attacks and Detection on Reinforcement Learning Based Interactive Recommender Systems
abstract: Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack agnostic detection on reinforcement learning based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks with a deep learning based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black box detector trained with one crafting method has the generalization ability over several crafting methods.

title: Distributed Generative Adversarial Networks for Anomaly Detection
abstract: Cognitive radio networks can be used to detect anomalous and adversarial communications to achieve situational awareness on the radio frequency spectrum. This paper proposes a distributed anomaly detection scheme based on adversarially trained data models. While many anomaly detection methods typically depend on a central decision making server, our distributed approach makes better use of decentralized resources, and decreases reliance on a single point of failure. Using a novel combination of generative adversarial network (GAN) elements, participating cognitive radio devices learn a representation of local network activity data through a non cooperative (strategic) game. Deviations from this expected network activity are flagged as anomalies and treated as possible network security threats, improving situational awareness. Tested on a range of time series datasets, the performance of the proposed distributed scheme matches that of state of the art, centralized anomaly detection methods.

title: Anomaly detection in milling tools using acoustic signals and generative adversarial networks
abstract: Acoustic monitoring presents itself as a flexible but under reported method of tool condition monitoring in milling operations. This paper demonstrates the power of the monitoring paradigm by presenting a method of characterizing milling tool conditions by detecting anomalies in the time frequency domain of the tools' acoustic spectrum during cutting operations. This is done by training a generative adversarial neural network on only a single, readily obtained class of acoustic data and then inverting the generator to perform anomaly detection. Anomalous and non anomalous data are shown to be nearly linearly separable using the proposed method, resulting in 90.56% tool condition classification accuracy and a 24.49% improvement over classification without the method.

title: On the robustness of skeleton detection against adversarial attacks
abstract: Human perception of an object's skeletal structure is particularly robust to diverse perturbations of shape. This skeleton representation possesses substantial advantages for parts based and invariant shape encoding, which is essential for object recognition. Multiple deep learning based skeleton detection models have been proposed, while their robustness to adversarial attacks remains unclear. (1) This paper is the first work to study the robustness of deep learning based skeleton detection against adversarial attacks, which are only slightly unlike the original data but still imperceptible to humans. We systematically analyze the robustness of skeleton detection models through exhaustive adversarial attacking experiments. (2) We propose a novel Frequency attack, which can directly exploit the regular and interpretable perturbations to sharply disrupt skeleton detection models. Frequency attack consists of an excitatory inhibition waveform with high frequency attribution, which confuses edge sensitive convolutional filters due to the sudden contrast between crests and troughs. Our comprehensive results verify that skeleton detection models are also vulnerable to adversarial attacks. The meaningful findings will inspire researchers to explore more potential robust models by involving explicit skeleton features.

title: Detection of Rogue RF Transmitters using Generative Adversarial Nets
abstract: Understanding and analyzing the radio frequency (RF) environment have become indispensable for various autonomous wireless deployments. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize a RF environment. However, classical machine learning methods have their limitations and there are situations where such methods become ineffective. One such setting is where active adversaries are present and try to disrupt the RF environment through malicious activities like jamming or spoofing. In this paper we propose an adversarial learning technique for identifying rogue RF transmitters and classifying trusted ones by designing and implementing generative adversarial nets (GAN) The GAN exploits the in phase (I) and quadrature imbalance (i.e. the IQ imbalance) present in all transmitters to learn the unique high dimensional features that can be used as "fingerprints" for identifying and classifying the transmitters. We implement a generative model that learns the sample space of the IQ values of the known transmitters and use the learned representation to generate fake signals that imitate the transmissions of the known transmitters. We program 8 universal software radio peripheral (USRP) software defined radios as trusted transmitters and collect over the air raw IQ data from them using a RTL SDR in a laboratory setting. We also implement a discriminator model and show that the discriminator is able to discriminate between the trusted transmitters from fake ones with 99.9% accuracy. Finally, the trusted transmitters are classified using convolutional neural network (CNN) and fully connected deep neural networks (DNN) Results reveal that the CNN and DNN are able to correctly discriminate between the 8 trusted transmitters with 81.6% and 96.6% accuracies respectively.

title: Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness
abstract: While research on adversarial examples in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work, with only one recent publication in the RF domain  RF adversarial examples (AdExs) can cause drastic, targeted misclassification results mostly in spectrum sensing/ survey applications (e.g. BPSK mistaken for 8 PSK) with minimal waveform perturbation. It is not clear if the RF AdExs maintain their effects in the physical world, i.e. when AdExs are delivered over the air (OTA) Our research on deep learning AdExs and proposed defense mechanisms are RF centric, and incorporate physical world, OTA effects. We here present defense mechanisms based on statistical tests. One test to detect AdExs utilizes Peak to Average Power Ratio (PAPR) of the DL data points delivered OTA, while another statistical test uses the Softmax outputs of the DL classifier, which corresponds to the probabilities the classifier assigns to each of the trained classes. The former test leverages the RF nature of the data, and the latter is universally applicable to AdExs regardless of their origin. Both solutions are shown as viable mitigation methods to subvert adversarial attacks against communications and radar sensing systems.