With the advent of Connected and Autonomous Vehicles (CAVs) comes the very real risk that these vehicles will be exposed to cyber-attacks by exploiting various vulnerabilities. This paper gives a technical overview of the H2020 CARAMEL project (currently in the intermediate stage) in which Artificial Intelligent (AI)-based cybersecurity for CAVs is the main goal. Most of the possible scenarios are considered, by which an adversary can generate attacks on CAVs, such as attacks on camera sensors, GPS location, Vehicle to Everything (V2X) message transmission, the vehicle's On-Board Unit (OBU), etc. The counter-measures to these attacks and vulnerabilities are presented via the current results in the CARAMEL project achieved by implementing the designed security algorithms.

This paper presents a new structure-based interest region detector called Shape-Stable Region Boundaries (SSRB) which we use for object class recognition. The SSRB interest operator detects stable boundary regions within the multi-scale morphological image representation [1]. To detect robust boundary regions, we perform multi-scale analysis via anisotropic diffusion operators to preserve boundaries and guarantee invariance to affine transformations. We extract the transition boundaries of the diffusivity velocity map and track their evolution at each level of the scale-space. The stability of the boundary shape is subsequently estimated through a minimization process over different scales. Unlike most state of the art detectors which use the Gaussian scale space for multi-scale image representation, our approach is intrinsically affine invariant [1]. Experiments on different benchmark datasets show that SSRB is comparable or superior to state-of the art detectors for both feature matching and object recognition.

In this paper we present a new approach towards the extraction of affine image regions based on detecting shape-stable boundaries from a multi-scale image representation. We construct an affine morphological scale space (AMSS) representation [1], which performs anisotropic diffusion while preserving boundaries and being invariant to affine transformations. We extract the transition boundaries of the diffusivity velocity map and track their evolution at each level of the scale-space. We then determine the stability of the boundary shape through a minimization process over different scales. Unlike most state of the art detectors which use the Gaussian scale space for multi-scale image representation, our approach is intrinsically affine invariant. We evaluate our detector by measuring repeatability of regions in transformed images of the same scene and comparing it to the state-of-the-art region detectors [2].