The Unbalanced Oil and Vinegar (UOV) construction is the foundation of several post-quantum digital signature algorithms currently under consideration in NIST’s standardization process for additional post-quantum digital signature schemes. This paper introduces new single fault injection attacks against the signing procedure of deterministic variants of signature schemes based on the UOV construction. We show how these attacks can be applied to attack MAYO and PROV, two signature schemes submitted to the NIST call for additional post-quantum signature schemes. The attacks are demonstrated with reference implementations that run on an ARM Cortex-M4 processor. Our attacks do not require precise triggering or precise fault injection capabilities. Any type of fault in large portions of the code has the potential to result in successful key recovery. We demonstrate our attacks with very cheap equipment and simple clock glitching techniques, enabling the recovery of the secret key with either two faulty signatures or one correct signature and one faulty signature in the case of MAYO and one correct signature and two faulty signatures in case of PROV. The fact that our attacks do not require precise fault injection capabilities and can be successful with only a few signatures makes them particularly powerful, hence harmful for the implementation security of post-quantum digital signature schemes.

Embedded devices commonly rely on digital signatures to ensure both integrity and authentication. For example, digital signatures are typically verified during the boot process or firmware updates to verify the integrity of a system. They are also used to ensure authenticity of a communication party in secure protocols. Fault injection can be used to tamper with a device in order to cause malfunctioning during cryptographic computations. For example, fault injections can be used to disturb digital signing operations. With the right type of fault an attacker can compute private keys from faulted signatures. However, fault injections can also be used during verification to get maliciously crafted digital signatures accepted during signature verification with catastrophic consequences for the security of an embedded device. In this paper, we introduce new non-obvious fault injection attacks on the verification routines of Dilithium and Falconsignature schemes, which allow an attacker to get signatures for arbitrary messages accepted by fault injection. We demonstrate the feasibility of our attacks by simulations using an ARM Cortex-M4 and the pqm4 library as a target of evaluation and pinpoint vulnerable instructions. Finally, we propose and discuss possible countermeasures against these attacks.

Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, Lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learning tasks, second, we introduce a baseline road detection on this dataset through supervised classification and hand-crafted feature vectors.

Vehicle localization using satellite navigation systems like GPS is a convenient and achievable solution. In order to solve this problem in dense urban areas, erroneous measurements caused by non-line-of-sight situations need to be handled carefully. The purpose of this paper is to evaluate shadow maps-an efficient representation of satellite reception conditions-for reliable real-time vehicle localization in urban areas. Instead of classical digital 3D maps which need to be installed and kept up-to-date at vehicle level, shadow maps can be exchanged on demand with vehicular ad-hoc networks. The proposed approach-developed within the European research project GAIN-is proven to increase accuracy and integrity of single-frequency GPS receivers in urban areas without additional physical sensors. In addition to the positioning performance, the necessary requirements for the communication channel are derived and compared to available technologies. The evaluated results are generated from different real-world test drives and compared to a high accurate ground truth.

Reliable knowledge of the ego position for vehicles is a crucial requirement for many automotive applications. In order to solve this problem for satellite-based localization in dense urban areas, multipath situations need to be handled carefully. This paper proposes a lightweight multipath detection algorithm which is based on dynamically built 3D environmental maps. The algorithm is evaluated with simulated and real-world data. Furthermore, it is applied to a combined GPS and GLONASS system in combination with a loosely coupled integration of odometry measurements from the vehicle.

Vehicle tracking under clutter is an important prerequisite for numerous vehicular applications. In this paper, we propose a generalization of the existing integrated probabilistic data association method in order to model situations where several true and additional clutter observations originated from one object. We will show that the proposed method outperforms the existing one. Furthermore, we will demonstrate a system utilizing a camera sensor and the proposed algorithm for detecting and tracking vehicles under clutter.

As-built reconstruction of existing industrial facilities such as power plants still involves much interactive work even though photogrammetry techniques are greatly used in this process. The majority of existing systems are based on 3D-point reconstruction. In general, these systems only use epipolar geometry to help the user. CyliCon is a software package for 3D reconstruction of industrial pipelines. This software enables its users to work with hundreds of images. It uses geometric features, such as occluding edges and vanishing points, and image processing methods, such as multi-resolution edge refinement, in order to provide semiautomatic user-friendly software. The software has been successfully tested on hundreds of indoor and outdoor industrial images.

We introduce a formula to compute an optimum 2-D shape-adaptive Karhunen-Loeve transform (KLT) suitable for coding pels in arbitrarily-shaped image segments. The efficiency of the KLT on a 2-D AR(1) process is used to benchmark two other shape-adaptive transforms described in literature. It is shown that the optimum KLT significantly outperforms the well known shape-adaptive DCT method introduced by Gilge et al. (1989) for coding Segments of arbitrary shape in intraframe coding mode. A statistical transform gain close to the Gilge-method can be achieved with a shape-adaptive DCT algorithm introduced by Sikora and Makai (see Proc. Workshop Image Anal. Image Coding, Berlin, FRG, Nov. 1993) which is implemented with much lower complexity.>