Authenticated Private Information Retrieval (Authenticated PIR) allows the client to retrieve the desired database entry without revealing any information about the query, while safely aborting if malicious behavior by the server is detected (presented in USENIX ’23). However, two key challenges remain: existing single-server authenticated PIR schemes with sublinear online communication have not yet been clearly and fully implemented; incremental updates to the digest introduce unnecessary overhead. In this paper, we implement the previously outlined idea and present two complete and concrete single-server authenticated PIR schemes with O(√(N)) online communication, namely LWE-AuthPIR and DDH-AuthPIR, along with detailed security proofs. Furthermore, we introduce the notion of single-server authenticated and incremental PIR and propose a corresponding concrete construction, LWE-AuthIncPIR. LWE-AuthIncPIR supports immediate updates to individual entries and integrates a communication-efficient row aggregation for periodic update scenarios. When 1 × compared to previous incremental update method. In the password breach detection, LWE-AuthIncPIR achieves a 67 × reduction in preprocessing time and a 2.9 × reduction in communication overhead.
With the continual evolution of network-attack techniques, conventional password-based authentication mechanisms have become increasingly ineffective when countering emerging cyber threats. To improve both security and usability, the FIDO Alliance introduced the FIDO2 authentication standard. As a passwordless solution, FIDO2 leverages public-key cryptography to mitigate risks such as credential leakage and to provide a more secure means of identity verification. Nevertheless, real-world FIDO2 deployments can still be exposed to latent security threats. To address this gap, we propose a FIDO2 security assessment framework grounded in a Markov Decision Process (MDP). By modelling the FIDO2 authentication flow as an MDP, the framework evaluates the in-transit security risk of WebAuthn communications and their associated verification parameters. It dynamically calculates the risk state of each authentication request in real time, adaptively escalates the strength of the FIDO2 authentication mechanism, and thus reinforces the transmission security of FIDO2-based identity verification.
We propose a self-supervised learning framework for identifying secondary compression artifacts in H.264 videos, which tackles the challenge of scarce labeled data in forensic analysis. The proposed method includes a Compression Artifact Prediction Module (CAPM) that trains a convolutional neural network (CNN) beforehand to estimate synthetic compression parameters from unlabeled video frames, which helps the model acquire discriminative features without requiring extensive annotations. The system produces synthetic artifacts by modeling controlled re-compression scenarios with different quantization parameters and frame rates, which act as self-supervision signals for pre-training. The CAPM employs a multi-task loss to jointly estimate compression parameters, frame type distributions, and motion vector magnitudes, thereby capturing intrinsic patterns of compression artifacts. Additionally, a module dedicated to temporal consistency combines spatiotemporal features from multiple frames by applying 3D convolutions, whereas a contrastive detection head improves the distinction between primary and secondary compression traces. The last detection phase merges transformer-driven self-attention and a binary classifier to capture extended dependencies and strengthen decision reliability. Experiments show that our method attains good performance in secondary compression detection while markedly decreasing dependence on annotated data.
Cross-chain asset bridges usually require the joint authorization of multiple nodes to execute transactions. Multi-signature, due to its ability to compress multiple signatures for the same message into a short signature, can be well applied to cross-chain asset bridges. The existing multi-signature schemes, however, based on distributed key generation (DKG) have obviously three vulnerabilities: the key generation stage incurs considerable interactive communications, a malicious centralized combiner enables to tamper with transaction content, and the signature scheme lacks the accountability function for signers. To solve these challenging issues, this paper constructs a new type of traceable multi-signature scheme TMDC (Traceable Multi-signature Scheme with Distributed Combiners). In this scheme, a fully decentralized key generation is exploited, where the signer node can independently generate public and private key pairs. To avoid the centralization of combiners, time-lock is used to limit the aggregation power of combiners. Once the time lock is unlocked, the combiner can no longer aggregate. Furthermore, TMDC designs the signature accountability algorithm and establishes a strong correlation mechanism between the signer’s behavior and the final multi-signature. Based on the specific properties above-mentioned, TMDC can be well applied to cross-chain asset bridges. TMDC not only enables distributed combiners to avoid the risks brought by only one combiner, but also can trace abnormal signers when the cross-chain asset bridge is attacked, significantly enhancing the security of the cross-chain asset bridge. Finally, after simultaneously implementing functions, TMDC still maintains a low communication and computational overhead. The verifying algorithm is superior to other related schemes, significantly enhancing the system efficiency.
Adversarial attacks induce misclassification in deep neural networks through imperceptible perturbations to human vision. Traffic sign images exhibit intrinsic structural vulnerabilities in decision boundaries due to their characteristic features—high-saturation color distributions and standardized geometric shapes. To address these vulnerabilities, this paper proposes a clean-sample guided adaptive overlapping patch defense method specifically designed for traffic sign recognition systems. Diverging from conventional perturbation-elimination paradigms, our approach constructs defense mechanism by exploiting the visual indistinguishability between adversarial and clean samples. A clean sample repository is established through K-means and Histogram of Oriented Gradients feature modeling, with rapid input-to-repository matching achieved via dual metrics of “structural similarity” and “perceptual hashing” joint similarity. Guided defense patches are dynamically generated based on the nearest clean samples. Experimental results on the GTSRB dataset show that our method maintains an original classification accuracy of 0.9838, merely 0.01 lower than the baseline model, while achieving a defensive accuracy of 0.7240 against PGD attacks, comparable to current state-of-the-art defense methods. Compared to the average 0.19 accuracy degradation of traditional methods, our approach effectively mitigates the trade-off conflict between model robustness and classification accuracy.
