The widespread of social networking services allows users to share and quickly spread an enormous amount of digital contents. Currently, a low level of security and trustworthiness is applied to such information, whose reliability cannot be taken for granted due to the large availability of image editing software which allow any user to easily manipulate digital contents. This has a huge impact on the deception of users, whose opinion can be seriously influenced by altered media. In this work, we face the challenge of verifying online news by analyzing the images related to the particular news article. Our goal is to create an empirical system which helps in verifying the consistency of visually and semantically similar images used within different news articles on the same topic. Given a certain news online, our system identifies a set of images connected to the same topic and presenting common visual elements, which can be successively compared with the original ones and analyzed in order to discover possible inconsistencies also by means of multimedia forensics tools.

Vehicular Ad-Hoc Networks (VANETs) offer enhanced road safety, efficient traffic management, and improved vehicle connectivity while dealing with privacy and security challenges in public communication. In these networks, authentication mechanisms are mandatory to establish trust among communicating entities, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), without losing identity and location-based privacy. The prevailing conventional authentication mechanisms frequently depend on a centralized trust authority (CA) to ensure the mutual verifiability of transmitted messages. Nevertheless, in scenarios where the density of vehicles within the network is notably high, an overwhelming influx of authentication requests may result in a communication bottleneck at the CA, leading to a single point of failure. This paper proposes a novel distributed authentication scheme in a decentralized VANET with multiple independent CAs connected to multiple local inspectors to eliminate a single point of failure. Furthermore, prior solutions lack the capability to immediately revoke a disputed vehicle that is transmitting malicious messages in the network. In this regard, the proposed scheme also facilitates an immediate revocation of a disputed sender to prevent other vehicles from further receiving malicious messages. As vehicles share time-sensitive data for driving assistance, our scheme minimizes the computation and communication costs for V2I key sharing and direct V2V authenticated message sharing significantly compared to previously proposed schemes. Using comparatively lightweight elliptic curve cryptography and eliminating the direct involvement of CAs in the authentication process, we have reduced the overall delays and achieved a maximum of $\approx 3.9$ times faster V2I authenticated key sharing, and a maximum of $\approx 7.5$ times faster V2V message sharing compared to state-of-the-art bilinear pairing-based protocols. A comprehensive efficiency analysis validates our scheme’s ability to outperform time-sensitive responses, such as sending and receiving an alert within nearly 4 milliseconds.

Cryptanalysis is the development and study of attacks against cryptographic primitives and protocols. Many cryptographic properties rely on the difficulty of generating an adversary who, given an object sampled from one of two classes, correctly distinguishes the class used to generate that object. In the case of cipher suite distinguishing problem, the classes are two different cryptographic primitives. In this paper, we propose a methodology based on machine learning to automatically generate classifiers that can be used by an adversary to solve any distinguishing problem. We discuss the assumptions, a basic approach for improving the advantage of the adversary as well as a phenomenon that we call the “blind spot paradox”. We apply our methodology to generate distinguishers for the NIST (DRBG) cipher suite problem. Finally, we provide empirical evidence that the distinguishers might statistically have some advantage to distinguish between the DRBG used.

Vehicular Ad Hoc Networks (VANETs) are the backbone of intelligent transport and enhanced passenger safety, but they face significant challenges related to authentication, security, and privacy. Existing distributed VANET authentication protocols struggle with issues like privacy preservation during vehicle handovers and inefficiency when handling large volumes of verifications. This paper proposes a novel authentication framework designed to address these limitations. First, we introduce zero-knowledge guarantees for Vehicle-to-Infrastructure (V2I) authentication and improve anonymity and unlinkability in authentication by eliminating explicit vehicle handover, thus enhancing privacy. Second, we propose a batch-verifiable Vehicle-to-Vehicle (V2V) message-sharing method utilizing an elliptic curve digital signatures scheme (ECDSA*). Unlike others, we provide a complete computational and efficiency analysis of batch verification in the presence of faulty signatures. A formal security analysis and proven security in the Scyther security verification tool provide the security guarantees of our proposed scheme. A thorough efficiency analysis shows that our scheme can perform at least 5-times more V2I authentication and can batch verify at least 2-times more V2V messages than other related schemes within a time threshold of 300ms.