The Verifiable Timed Signature achieves the delayed disclosure of the signature by introducing a time-delay mechanism, and allows the verifier to verify or restore the signature after meeting the predetermined time conditions. The existing VTS schemes mainly focus on the delayed disclosure of the signature and the enforcement of it, providing effective solutions for signature scenarios with time constraints. However, the traditional VTS lacks the ability to dynamically manage the state of specific signatures that have been submitted but not yet disclosed, making it difficult to meet the requirements for signature revocation in some application scenarios. To address these issues, we formally propose the Revocable Verifiable Timed Signature, provide its formal definition and security model, and construct a specific instantiation scheme. The security analysis shows that this instantiation scheme satisfies the security properties such as correctness, unforgeability, soundness, privacy, policy soundness, and policy completeness under the defined security model. Based on this, we abstract the revocation behavior as a computable policy function, adjusts the definition of the policy function to achieve different revocation strategies, and provides corresponding application scenarios for different revocation strategies, thereby providing a more flexible signature state management mechanism for timed signatures.
To address the difficulty of conventional fixed antennas in simultaneously ensuring satisfactory coverage performance for both aerial users (AUEs) and ground users (GUEs) in low-altitude intelligent networks, a three-dimensional (3D) coverage optimization method based on a reconfigurable waveguide-fed pinching-antenna (PA) system is proposed. First, by jointly optimizing PA positions along the waveguides and the waveguide power allocation, a coverage optimization model is formulated to maximize the weighted spectral efficiency of AUEs and GUEs, subject to the total system power constraint, the minimum signal-to-interference-plus-noise ratio (SINR) constraints of users, and the PA geometric constraints. Second, to address the non-convexity and non-smoothness caused by the discrete association between users and waveguides, a soft association mechanism is introduced to obtain a smooth approximation of the optimization problem, while a variable-shift transformation is employed to effectively handle the PA geometric constraints. Finally, a joint optimization algorithm for PA positioning and waveguide power allocation is developed based on the primal–dual projected gradient method. Simulation results demonstrate that the proposed algorithm can significantly improve the SINR and spectral efficiency of both AUEs and GUEs, thereby achieving balanced 3D coverage enhancement.
To address the issues of high visibility, limited generalization, and the trade-off between attack effectiveness and clean performance in lightweight multi-label image classification, a backdoor attack method was proposed by integrating a content-adaptive deep steganographic trigger with multi-label conditional generation. A shallow convolution-based trigger encoder was used to generate amplitude-constrained pixel-level residuals according to input content, and a multi-label conditional StyleGAN2-ADA was introduced to generate auxiliary samples based on real label distributions. Real and generated triggered samples were jointly optimized with the classifier and trigger encoder. Experiments on two datasets achieved target attack success rates of 98.21% and 99.47%, with mAP decreases of only 0.48 and 0.43 percentage points and false activation rates of 3.96% and 3.12%, respectively. The triggered images achieved PSNR values of 40.76 and 41.18 dB, SSIM values of 0.9928 and 0.9934, and LPIPS values of 0.014 and 0.013. Heterogeneous transfer experiments maintained attack success rates above 93.9%. The proposed method provides an effective and stealthy backdoor attack framework for lightweight multi-label image classification.
To address the vulnerabilities, interruptions, and limited onboard resources of low earth orbit (LEO) satellite channels, a trusted terminal access authentication and key agreement protocol for integrated space-ground networks was proposed, enabling secure and efficient two-way authentication and key exchange between end users and LEO satellites. The protocol employed lightweight cryptographic primitives, with the ground control center handling most of the computational tasks. Pseudonyms and identity encryption were designed to protect user privacy. Authentication parameters generated during initial access enable rapid re-authentication by the satellite after disconnection, improving the efficiency of reconnection. Security analysis using the real-or-random (ROR) model and the Tamarin tool demonstrates that the protocol offers stronger security and privacy protection. Performance evaluation shows that it outperforms existing schemes in terms of computational overhead and communication efficiency, meeting the lightweight requirements of integrated space-ground networks.
While the booming low-altitude economy accelerates UAV adoption, unauthorized flights increasingly threaten urban safety, giving rise to collision, privacy‑related, and security risks. Addressing this emerging airspace environment, anti-UAV technologies were reviewed systematically. Firstly, key urban low-altitude challenges were outlined: complex sensing environments, difficult target identification, and limited countermeasure options. Subsequently, mainstream technologies were reviewed across three stages: sensing (radar, RF, optical/infrared, acoustics, and multimodal fusion), identification (identity- and behavior-based methods), and countermeasures (jamming, spoofing/hijacking, physical capture, and kinetic damage). Meanwhile, core evaluation metrics and experimental setups were summarized, highlighting the gap between academia and industry. Finally, future trends were discussed toward networked sensing, intelligent identification, and collaborative countermeasures.
