Traditional Camouflaged Object Detection (COD) methods heavily depend on labor-intensive annotated datasets which require extensive manual effort, resulting in limited generalization. While recent studies have combined Multimodal Large Language Models (MLLMs) and Vision Foundation Models (VFMs) to achieve zero-shot COD, their performance is hindered by modality gap between linguistic semantics and fine-grained visual cues, especially in complex camouflage scenarios. In this paper, we propose Language-to-instance generative visual Prompting (LiP), a novel framework that addresses this limitation by transforming text prompts generated by MLLMs into instance-level visual prompts through a text-to-image generative process. Specifically, we introduce a Diffusion-driven Visual Prompt Generation (DVPG) module that leverages Stable Diffusion model to synthesize visual references, enabling robust homogeneous modality matching for COD. Additionally, we introduce Instruction Contrastive Reasoning (ICR) module to enhance the semantic reliability of prompts by suppressing hallucinated concepts during MLLM inference. To the best of our knowledge, LiP is the first framework that utilize text-to-image generative model to construct instance-level visual prompts in COD task. Extensive experiments on four benchmark datasets demonstrate the effectiveness and strong generalization ability of our approach.

Connected and autonomous vehicles produce a substantial amount of data that is essential for implementing advanced and intelligent features. Given the importance and the volume of in-vehicle data, storing it in the cloud for later extraction as critical evidence for vehicle digital forensics is a logical choice. However, ensuring the security of forensic data against tampering and forgery attacks throughout the process is a significant challenge. Existing solutions typically assume that vehicles will generate and upload the in-vehicle data to the cloud honestly. In reality, it may be necessary to prove whether the vehicle has uploaded authentic driving-related data in case of disputes about data authenticity. To address this issue, we propose an in-vehicle digital forensic scheme with public auditing, enabling anyone to perform a public auditing algorithm to check whether the data has been modified. The proposal is based on a process-oriented data integrity proof method that enables a vehicle to generate public verifiable integrity proof. Furthermore, we evaluated the practicality of our scheme by assessing its computational and communication overhead. In terms of computational cost, our proposed scheme demonstrates a power consumption of 0.0385 kWh per 100 km at a speed of 60 km/h. Regarding communication delay, our method exhibits a 50.1% decrease compared to similar approaches.

The security and privacy challenges faced by Vehicular Ad hoc Networks (VANETs) have led to the development of conditional privacy-preserving authentication (CPPA) schemes. Hardware security modules (HSMs) are seen as a promising solution for implementing these schemes while minimizing the burden on certificate storage. However, existing HSM-based CPPA schemes still have high computation overhead and do not meet the forward security requirements for system secret key (SSK) updates. To address these challenges, we propose an HSM-based lightweight CPPA scheme for VANETs that enjoy low computation costs. Most operations could be performed within the HSM before the message is ready to be signed, reducing real-time computation delay. The scheme also supports SSK updating using an identity-based batch multi-signature algorithm, which helps to provide forward security and vehicle revocation. Especially, the proposed SSK update scheme does not rely on any single trusted authority. Formal proof demonstrates that the proposed scheme satisfies the desired security notions. Our analysis shows that this scheme surpasses other similar ones in terms of efficiency when it comes to generating signatures.