Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding

Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times.Comment: This paper has been published in Scientific Reports Journa

Adversarial Audio: A New Information Hiding Method and Backdoor for DNN-based Speech Recognition Models

Audio is an important medium in people's daily life, hidden information can be embedded into audio for covert communication. Current audio information hiding techniques can be roughly classed into time domain-based and transform domain-based techniques. Time domain-based techniques have large hiding capacity but low imperceptibility. Transform domain-based techniques have better imperceptibility, but the hiding capacity is poor. This paper proposes a new audio information hiding technique which shows high hiding capacity and good imperceptibility. The proposed audio information hiding method takes the original audio signal as input and obtains the audio signal embedded with hidden information (called stego audio) through the training of our private automatic speech recognition (ASR) model. Without knowing the internal parameters and structure of the private model, the hidden information can be extracted by the private model but cannot be extracted by public models. We use four other ASR models to extract the hidden information on the stego audios to evaluate the security of the private model. The experimental results show that the proposed audio information hiding technique has a high hiding capacity of 48 cps with good imperceptibility and high security. In addition, our proposed adversarial audio can be used to activate an intrinsic backdoor of DNN-based ASR models, which brings a serious threat to intelligent speakers.Comment: Submitted to RAID201

Achievable Rates of Attack Detection Strategies in Echo-Assisted Communication

We consider an echo-assisted communication model wherein block-coded messages, when transmitted across several frames, reach the destination as multiple noisy copies. We address adversarial attacks on such models wherein a subset of the noisy copies are vulnerable to manipulation by an adversary. Particularly, we study a non-persistent attack model with the adversary attacking 50% of the frames on the vulnerable copies in an i.i.d. fashion. We show that this adversarial model drives the destination to detect the attack locally within every frame, thereby resulting in degraded performance due to false-positives and miss-detection. Our main objective is to characterize the mutual information of this adversarial echo-assisted channel by incorporating the performance of attack-detection strategies. With the use of an imperfect detector, we show that the compound channel comprising the adversarial echo-assisted channel and the attack detector exhibits memory-property, and as a result, obtaining closed-form expressions on mutual information is intractable. To circumvent this problem, we present a new framework to approximate the mutual information by deriving sufficient conditions on the channel parameters and also the performance of the attack detectors. Finally, we propose two attack-detectors, which are inspired by traditional as well as neural-network ideas, and show that the mutual information offered by these detectors is close to that of the Genie detector for short frame-lengths.Comment: 6 pages and 3 figure

Vesper: Using Echo-Analysis to Detect Man-in-the-Middle Attacks in LANs

The Man-in-the-Middle (MitM) attack is a cyber-attack in which an attacker intercepts traffic, thus harming the confidentiality, integrity, and availability of the network. It remains a popular attack vector due to its simplicity. However, existing solutions are either not portable, suffer from a high false positive rate, or are simply not generic. In this paper, we propose Vesper: a novel plug-and-play MitM detector for local area networks. Vesper uses a technique inspired from impulse response analysis used in the domain of acoustic signal processing. Analogous to how echoes in a cave capture the shape and construction of the environment, so to can a short and intense pulse of ICMP echo requests model the link between two network hosts. Vesper uses neural networks called autoencoders to model the normal patterns of the echoed pulses, and detect when the environment changes. Using this technique, Vesper is able to detect MitM attacks with high accuracy while incurring minimal network overhead. We evaluate Vesper on LANs consisting of video surveillance cameras, servers, and PC workstations. We also investigate several possible adversarial attacks against Vesper, and demonstrate how Vesper mitigates these attacks

Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding

Voice interfaces are becoming accepted widely as input methods for a diverse set of devices. This development is driven by rapid improvements in automatic speech recognition (ASR), which now performs on par with human listening in many tasks. These improvements base on an ongoing evolution of DNNs as the computational core of ASR. However, recent research results show that DNNs are vulnerable to adversarial perturbations, which allow attackers to force the transcription into a malicious output. In this paper, we introduce a new type of adversarial examples based on psychoacoustic hiding. Our attack exploits the characteristics of DNN-based ASR systems, where we extend the original analysis procedure by an additional backpropagation step. We use this backpropagation to learn the degrees of freedom for the adversarial perturbation of the input signal, i.e., we apply a psychoacoustic model and manipulate the acoustic signal below the thresholds of human perception. To further minimize the perceptibility of the perturbations, we use forced alignment to find the best fitting temporal alignment between the original audio sample and the malicious target transcription. These extensions allow us to embed an arbitrary audio input with a malicious voice command that is then transcribed by the ASR system, with the audio signal remaining barely distinguishable from the original signal. In an experimental evaluation, we attack the state-of-the-art speech recognition system Kaldi and determine the best performing parameter and analysis setup for different types of input. Our results show that we are successful in up to 98% of cases with a computational effort of fewer than two minutes for a ten-second audio file. Based on user studies, we found that none of our target transcriptions were audible to human listeners, who still understand the original speech content with unchanged accuracy

Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, scan time limitations may prohibit acquisition of certain contrasts, and images for some contrast may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts from remaining contrasts can improve diagnostic utility. For multi-contrast synthesis, current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can in turn suffer from loss of high-spatial-frequency information in synthesized images. Here we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves high-frequency details via an adversarial loss; and it offers enhanced synthesis performance via a pixel-wise loss for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improved synthesis quality. Demonstrations on T1- and T2-weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to previous state-of-the-art methods. Our synthesis approach can help improve quality and versatility of multi-contrast MRI exams without the need for prolonged examinations

Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver

Purpose: To improve the quality of images obtained via dynamic contrast-enhanced MRI (DCE-MRI) that include motion artifacts and blurring using a deep learning approach. Methods: A multi-channel convolutional neural network (MARC) based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T1-weighted spoiled gradient echo sequence for the liver containing breath-hold failures during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland-Altman plots. Results: The proposed network was found to significantly reduce the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images. Conclusion: A deep learning based method for removing motion artifacts in images obtained via DCE-MRI in the liver was demonstrated and validated.Comment: 11 pages, 6 figure

Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction

Many real-world signal sources are complex-valued, having real and imaginary components. However, the vast majority of existing deep learning platforms and network architectures do not support the use of complex-valued data. MRI data is inherently complex-valued, so existing approaches discard the richer algebraic structure of the complex data. In this work, we investigate end-to-end complex-valued convolutional neural networks - specifically, for image reconstruction in lieu of two-channel real-valued networks. We apply this to magnetic resonance imaging reconstruction for the purpose of accelerating scan times and determine the performance of various promising complex-valued activation functions. We find that complex-valued CNNs with complex-valued convolutions provide superior reconstructions compared to real-valued convolutions with the same number of trainable parameters, over a variety of network architectures and datasets

Male pelvic synthetic CT generation from T1-weighted MRI using 2D and 3D convolutional neural networks

To achieve magnetic resonance (MR)-only radiotherapy, a method needs to be employed to estimate a synthetic CT (sCT) for generating electron density maps and patient positioning reference images. We investigated 2D and 3D convolutional neural network (CNN) methods to generate a male pelvic sCT using a T1-weighted MR image. A retrospective study was performed using CTs and T1-weighted MR images of 20 prostate cancer patients. The proposed 2D CNN model, which contained 27 convolutional layers, was modified from the SegNet for better performance. 3D version of the CNN model was also developed. Both CNN models were trained from scratch to map intensities of T1-weighted MR images to CT Hounsfield Unit (HU) values. Each sCT was generated in a five-fold-cross-validation framework and compared with the corresponding CT using voxel-wise mean absolute error (MAE), and dice similarity coefficient (DSC), recall, and precision for bony structures. Wilcoxon signed-rank tests were performed to evaluate the differences between the both models. The MAE averaged across all patients were 40.5 $\pm$ 5.4 HU and 37.6 $\pm$ 5.1 HU for the 2D and 3D CNN models, respectively. The DSC, recall, and precision of the bony structures were 0.81 $\pm$ 0.04, 0.85 $\pm$ 0.04, and 0.77 $\pm$ 0.09 for the 2D CNN model, and 0.82 $\pm$ 0.04, 0.84 $\pm$ 0.04, and 0.80 $\pm$ 0.08 for the 3D CNN model, respectively. P values of the Wilcoxon signed-rank tests were less than 0.05 except for recall, which was 0.6. The 2D and 3D CNN models generated accurate pelvic sCTs for the 20 patients using T1-weighted MR images. The evaluation metrics and statistical tests indicated that the 3D model was able to generate sCTs with better MAE, bone DSC, and bone precision. The accuracy of the dose calculation and patient positioning using generated sCTs will be tested and compared for the two models in the future.Comment: Medical Physics 201

An Overview of Vulnerabilities of Voice Controlled Systems

Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. However, how exactly these techniques differ or relate to each other has not been extensively studied. In this paper, we provide a survey of recent attack and defense techniques for voice controlled systems and propose a classification of these techniques. We also discuss the need for a universal defense strategy that protects a system from various types of attacks.Comment: 1st International Workshop on Security and Privacy for the Internet-of-Things (IoTSec