Hadi Zanddizari

G. Dumindu Samaraweera, Hung Nguyen, Hadi Zanddizari et al.

Recent breakthrough technological progressions of powerful mobile computing resources such as low-cost mobile GPUs along with cutting-edge, open-source software architectures have enabled high-performance deep learning on mobile platforms. These advancements have revolutionized the capabilities of today's mobile applications in different dimensions to perform data-driven intelligence locally, particularly for smart health applications. Unlike traditional machine learning (ML) architectures, modern on-device deep learning frameworks are proficient in utilizing computing resources in mobile platforms seamlessly, in terms of producing highly accurate results in less inference time. However, on the flip side, energy resources in a mobile device are typically limited. Hence, whenever a complex Deep Neural Network (DNN) architecture is fed into the on-device deep learning framework, while it achieves high prediction accuracy (and performance), it also urges huge energy demands during the runtime. Therefore, managing these resources efficiently within the spectrum of performance and energy efficiency is the newest challenge for any mobile application featuring data-driven intelligence beyond experimental evaluations. In this paper, first, we provide a timely review of recent advancements in on-device deep learning while empirically evaluating the performance metrics of current state-of-the-art ML architectures and conventional ML approaches with the emphasis given on energy characteristics by deploying them on a smart health application. With that, we are introducing a new framework through an energy-aware, adaptive model comprehension and realization (EAMCR) approach that can be utilized to make more robust and efficient inference decisions based on the available computing/energy resources in the mobile device during the runtime.

Hadi Zanddizari, Sreeraman Rajan, Hassan Rabah et al.

Cloud computing for storing data and running complex algorithms have been steadily increasing. As connected IoT devices such as wearable ECG recorders generally have less storage and computational capacity, acquired signals get sent to a remote center for storage and possible analysis on demand. Recently, compressive sensing (CS) has been used as secure, energy-efficient method of signal sampling in such recorders. In this paper, we propose a secure procedure to outsource the total recovery of CS measurement to the cloud and introduce a privacy-assured signal recovery technique in the cloud. We present a fast, and lightweight encryption for secure CS recovery outsourcing that can be used in wearable devices, such as ECG Holter monitors. In the proposed technique, instead of full recovery of CS-compressed ECG signal in the cloud, to preserve privacy, an encrypted version of ECG signal is recovered by using a randomly bipolar permuted measurement matrix. The user with a key, decrypts the encrypted ECG from the cloud to obtain the original ECG signal. We demonstrate our proposed method using the ECG signals available in the MITBIH Arrhythmia Database. We also demonstrate the strength of the proposed method against partial exposure of the key.

Hadi Zanddizari, Behnam Zeinali, J. Morris Chang

Applications of machine learning (ML) models and convolutional neural networks (CNNs) have been rapidly increased. Although state-of-the-art CNNs provide high accuracy in many applications, recent investigations show that such networks are highly vulnerable to adversarial attacks. The black-box adversarial attack is one type of attack that the attacker does not have any knowledge about the model or the training dataset, but it has some input data set and their labels. In this paper, we propose a novel approach to generate a black-box attack in sparse domain whereas the most important information of an image can be observed. Our investigation shows that large sparse (LaS) components play a critical role in the performance of image classifiers. Under this presumption, to generate adversarial example, we transfer an image into a sparse domain and put a threshold to choose only k LaS components. In contrast to the very recent works that randomly perturb k low frequency (LoF) components, we perturb k LaS components either randomly (query-based) or in the direction of the most correlated sparse signal from a different class. We show that LaS components contain some middle or higher frequency components information which leads fooling image classifiers with a fewer number of queries. We demonstrate the effectiveness of this approach by fooling six state-of-the-art image classifiers, the TensorFlow Lite (TFLite) model of Google Cloud Vision platform, and YOLOv5 model as an object detection algorithm. Mean squared error (MSE) and peak signal to noise ratio (PSNR) are used as quality metrics. We also present a theoretical proof to connect these metrics to the level of perturbation in the sparse domain.