Universal Fourier Attack for Time Series

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines

ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images

In the last decade, Convolutional Neural Network (CNN) and transformer based object detectors have achieved high performance on a large variety of datasets. Though the majority of detection literature has developed this capability on datasets such as MS COCO, these detectors have still proven effective for remote sensing applications. Challenges in this particular domain, such as small numbers of annotated objects and low object density, hinder overall performance. In this work, we present a novel augmentation method, called collage pasting, for increasing the object density without a need for segmentation masks, thereby improving the detector performance. We demonstrate that collage pasting improves precision and recall beyond related methods, such as mosaic augmentation, and enables greater control of object density. However, we find that collage pasting is vulnerable to certain out-of-distribution shifts, such as image corruptions. To address this, we introduce two simple approaches for combining collage pasting with PixMix augmentation method, and refer to our combined techniques as ColMix. Through extensive experiments, we show that employing ColMix results in detectors with superior performance on aerial imagery datasets and robust to various corruptions

An Ecient and Secure Transmission Method Using Data Partitioning for AMI in Smart Grids

Advanced Metering Infrastructure (AMI) has been rapidly developed and widely used for the utility industry; meanwhile, it also has become an attractive target of different varieties of cyber-attacks due to AMI\u27s security and privacy vulnerabilities as well as providing a way where one may steal energy. Therefore, it is crucial to develop a reliable, secure and efficient AMI network system with privacy protection. In this thesis, we introduce our data partitioning network system that splits the data into two separate partitions and transmits on one data channel with a privacy protection mechanism, an effective energy theft detection analyzer, a secure key exchange protocol, and a collaborative intrusion detection system in order to collect, transmit, manage, analyze and store energy information for the advanced metering infrastructure in smart grids. Security, privacy and energy theft are three main threats for AMI system. Our proposed method allows the server to check the integrity without decrypting the message by using homomorphic encryption techniques. Additionally, our anomaly-based energy theft detection method detects energy theft using fuzzy clustering techniques from data mining which has a minimum accuracy of 95\%. A collaborative intrusion detection system that distributes various detection techniques with different levels of computation complexity into different parts of the AMI network communication system is discussed. With the help of an encryption key exchange protocol and the collaborative intrusion detection system, it is shown that a potential access point denial-of-service attack triggered by a single smart meter can occur and a possible solution to mitigate the attack is provided. Simulation and analytical results show that our AMI network system design can provide secure, private and efficient communication with reasonable delay and overheads

Image Compression and Channel Error Correction using Neurally-Inspired Network Models

Everyday an enormous amount of information is stored, processed and transmitted digitally around the world. Neurally-inspired compression models have been rapidly developed and researched as a solution to image processing tasks and channel error correction control. This dissertation presents a deep neural network (DNN) for gray high-resolution image compression and a fault-tolerant transmission system with channel error-correction capabilities. A feed-forward DNN implemented with the Levenberg-Marguardt learning algorithm is proposed and implemented for image compression. I demonstrate experimentally that the DNN not only provides better quality reconstructed images but also requires less computational capacity as compared to DCT Zonal coding, DCT Threshold coding, Set Partitioning in Hierarchical Trees (SPIHT) and Gaussian Pyramid. An artificial neural network (ANN) with improved channel error-correction rate is also proposed. The experimental results indicate that the implemented artificial neural network provides a superior error-correction ability by transmitting binary images over the noisy channel using Hamming and Repeat-Accumulate coding. Meanwhile, the network’s storage requirement is 64 times less than the Hamming coding and 62 times less than the Repeat-Accumulate coding. Thumbnail images contain higher frequencies and much less redundancy, which makes them more difficult to compress compared to high-resolution images. Bottleneck autoencoders have been actively researched as a solution to image compression tasks. However, I observed that thumbnail images compressed at a 2:1 ratio through bottleneck autoencoders often exhibit subjectively low visual quality. In this dissertation, I compared bottleneck autoencoders with two sparse coding approaches. Either 50\% of the pixels are randomly removed or every other pixel is removed, each achieving a 2:1 compression ratio. In the subsequent decompression step, a sparse inference algorithm is used to in-paint the missing the pixel values. Compared to bottleneck autoencoders, I observed that sparse coding with a random dropout mask yields decompressed images that are superior based on subjective human perception yet inferior according to pixel-wise metrics of reconstruction quality, such as PSNR and SSIM. With a regular checkerboard mask, decompressed images were superior as assessed by both subjective and pixel-wise measures. I hypothesized that alternative feature-based measures of reconstruction quality would better support my subjective observations. To test this hypothesis, I fed thumbnail images processed using either bottleneck autoencoder or sparse coding using either checkerboard or random masks into a Deep Convolutional Neural Network (DCNN) classifier. Consistent, with my subjective observations, I discovered that sparse coding with checkerboard and random masks support on average 2.7\% and 1.6\% higher classification accuracy and 18.06\% and 3.74\% lower feature perceptual loss compared to bottleneck autoencoders, implying that sparse coding preserves more feature-based information. The optic nerve transmits visual information to the brain as trains of discrete events, a low-power, low-bandwidth communication channel also exploited by silicon retina cameras. Extracting high-fidelity visual input from retinal event trains is thus a key challenge for both computational neuroscience and neuromorphic engineering. % Here, we investigate whether sparse coding can enable the reconstruction of high-fidelity images and video from retinal event trains. Our approach is analogous to compressive sensing, in which only a random subset of pixels are transmitted and the missing information is estimated via inference. We employed a variant of the Locally Competitive Algorithm to infer sparse representations from retinal event trains, using a dictionary of convolutional features optimized via stochastic gradient descent and trained in an unsupervised manner using a local Hebbian learning rule with momentum. Static images, drawn from the CIFAR10 dataset, were passed to the input layer of an anatomically realistic retinal model and encoded as arrays of output spike trains arising from separate layers of integrate-and-fire neurons representing ON and OFF retinal ganglion cells. The spikes from each model ganglion cell were summed over a 32 msec time window, yielding a noisy rate-coded image. Analogous to how the primary visual cortex is postulated to infer features from noisy spike trains in the optic nerve, we inferred a higher-fidelity sparse reconstruction from the noisy rate-coded image using a convolutional dictionary trained on the original CIFAR10 database. Using a similar approach, we analyzed the asynchronous event trains from a silicon retina camera produced by self-motion through a laboratory environment. By training a dictionary of convolutional spatiotemporal features for simultaneously reconstructing differences of video frames (recorded at 22HZ and 5.56Hz) as well as discrete events generated by the silicon retina (binned at 484Hz and 278Hz), we were able to estimate high frame rate video from a low-power, low-bandwidth silicon retina camera

Characteristics of articulatory gestures in stuttered speech: a case study using real-time magnetic resonance imaging

Introduction : Most of the previous articulatory studies of stuttering have focussed on the fluent speech of people who stutter. However, to better understand what causes the actual moments of stuttering, it is necessary to probe articulatory behaviors during stuttered speech. We examined the supralaryngeal articulatory characteristics of stuttered speech using real-time structural magnetic resonance imaging (RT-MRI). We investigated how articulatory gestures differ across stuttered and fluent speech of the same speaker. Methods : Vocal tract movements of an adult man who stutters during a pseudoword reading task were recorded using RT-MRI. Four regions of interest (ROIs) were defined on RT-MRI image sequences around the lips, tongue tip, tongue body, and velum. The variation of pixel intensity in each ROI over time provided an estimate of the movement of these four articulators. Results : All disfluencies occurred on syllable-initial consonants. Three articulatory patterns were identified. Pattern 1 showed smooth gestural formation and release like fluent speech. Patterns 2 and 3 showed delayed release of gestures due to articulator fixation or oscillation respectively. Block and prolongation corresponded to either pattern 1 or 2. Repetition corresponded to pattern 3 or a mix of patterns. Gestures for disfluent consonants typically exhibited a greater constriction than fluent gestures, which was rarely corrected during disfluencies. Gestures for the upcoming vowel were initiated and executed during these consonant disfluencies, achieving a tongue body position similar to the fluent counterpart. Conclusion : Different perceptual types of disfluencies did not necessarily result from distinct articulatory patterns, highlighting the importance of collecting articulatory data of stuttering. Disfluencies on syllable-initial consonants were related to the delayed release and the overshoot of consonant gestures, rather than the delayed initiation of vowel gestures. This suggests that stuttering does not arise from problems with planning the vowel gestures, but rather with releasing the overly constricted consonant gestures

Dictionary Learning with Accumulator Neurons

The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (\hbox{S-LCA}) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (\hbox{LIF}) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras