In the last decades, machine learning techniques have been extensively used in place of classical template attacks to implement profiled side-channel analysis. This manuscript focuses on the application of machine learning to counteract Dynamic Frequency Scaling defenses. While state-of-the-art attacks have shown promising results against desynchronization countermeasures, a robust attack strategy has yet to be realized. Motivated by the simplicity and effectiveness of template attacks for devices lacking desynchronization countermeasures, this work presents a Deep Learning-assisted Template Attack (DLaTA) methodology specifically designed to target highly desynchronized traces through Dynamic Frequency Scaling. A deep learning-based pre-processing step recovers information obscured by desynchronization, followed by a template attack for key extraction. Specifically, we developed a three-stage deep learning pipeline to resynchronize traces to a uniform reference clock frequency. The experimental results on the AES cryptosystem executed on a RISC-V System-on-Chip reported a Guessing Entropy equal to 1 and a Guessing Distance greater than 0.25. Results demonstrate the method's ability to successfully retrieve secret keys even in the presence of high desynchronization. As an additional contribution, we publicly release ourDFS_DESYNCHdatabase11https://github.com/hardware-fab/DLaTAcontaining the first set of real-world highly desynchronized power traces from the execution of a software AES cryptosystem.

Side-channel attacks allow extracting secret infor-mation from the execution of cryptographic primitives by cor-relating the partially known computed data and the measured side-channel signal. However, to set up a successful side-channel attack, the attacker has to perform i) the challenging task of locating the time instant in which the target cryptographic primitive is executed inside a side-channel trace and then ii) the time-alignment of the measured data on that time instant. This paper presents a novel deep-learning technique to locate the time instant in which the target computed cryptographic operations are executed in the side-channel trace. In contrast to state-of-the-art solutions, the proposed methodology works even in the presence of trace deformations obtained through random delay insertion techniques. We validated our proposal through a successful attack against a variety of unprotected and protected cryptographic primitives that have been executed on an FPGA-implemented system-on-chip featuring a RISC- V CPU.

This paper presents an innovative approach to gaze tracking in the context of smart eyewear, utilizing a fully event-based algorithm. Traditional gaze-tracking methods often rely on grayscale or infrared imaging, which can be computationally intensive and raise privacy concerns. Our research addresses these issues by developing an algorithm that exclusively uses data from event-based sensors, optimizing for the limited computational capabilities of smart eyewear. The system uses simple geometrical operations, enabling efficient real-time processing. Experimental results demonstrate the feasibility of this approach, offering a promising solution for gaze tracking in compact, computationally constrained devices. Despite certain limitations in accuracy due to optimization for efficiency, the research underscores the practicality of this approach for practical, privacy-conscious applications in smart eyewear technology.

Synthetic Aperture Radar (SAR) is a cutting-edge remote sensing technology that offers a unique perspective on the Earth's surface through its advanced microwave imaging, providing valuable insights into various aspects of the environment. However, SAR images are impacted by the speckle phenomenon, which acts as noise, hindering accurate interpretation of the scene and presenting a major challenge for SAR image analysis and understanding. Deep Learning has emerged as a powerful solution for despeckling SAR images, but acquiring large amounts of labeled data for training is a significant obstacle as obtaining ground truth in the SAR domain is not feasible. The proposed method overcomes this limitation with a novel unsupervised approach to single-look SAR image despeckling. Our method, CycleSAR, leverages the power of cycle-consistent generative adversarial networks (CycleGANs) to formulate the despeckling problem as an unpaired image-to-image translation, by effectively bypassing the need for ground truth data. Additionally, our method not only effectively reduces the speckle in single-look SAR images, but also enables, by construction, the simultaneous learning of a generative model to generate realistic speckled realizations of multi-look SAR images. The addition of a conditional variational autoencoder (CVAE) further enhances the method, enabling the one-to-many generation of speckled images and leading to an overall improvement in despeckling performance. The experimental results demonstrate the remarkable capabilities of the proposed method, CycleSAR, in providing high-quality despeckling and realistic speckle realizations. CycleSAR stands apart from existing state-of-the-art methods as it does not rely on data simulation or make any assumptions about the speckle distribution.

Interferometric SAR (InSAR) algorithms exploit synthetic aperture radar (SAR) images to estimate ground displacements, which are updated at each new satellite acquisition, over wide areas. The analysis of the resulting time series finds its application, among others, in monitoring tasks regarding seismic faults, subsidence, landslides, and urban structures, for which an accurate and timely response is required. Typical analyses consist of identifying among the numerous time series the ones that exhibit an anomalous displacement, thus deserving to be further investigated. In practice, this is realized by selecting the time series that is characterized by trend changes w.r.t. the historical behavior. In this work, we propose a deep learning approach for change point detection in the InSAR time series. The designed architecture combines long short-term memory (LSTM) cells, to model the temporal correlation among samples in the input time series, and time-gated LSTM (TGLSTM) cells, to consider the sampling rate as additional information during learning. We further propose a solution to the lack of ground truth by developing a suitable pipeline for realistic data simulation. The method has been developed and validated through a large suite of experiments. Both quantitative and qualitative analyses have been conducted to demonstrate the detection capabilities of the learned model and how it is a valid alternative to the statistical reference algorithm. We further applied the developed method in a real continuous monitoring project to analyze the InSAR time series over the Tuscany region in Italy, proving its effectiveness in the real domain.