arXiv subjects

Phone Lin

Publications and source records attributed to Phone Lin.

4 recordsLinked to original sources

arXiv2026

A Novel Semantic Manifold Alignment Attack against Embedding-to-Embedding Obfuscation in Privacy-Preserving LLMs

With the widespread applications of large language models (LLMs), privacy-preserving inference has become increasingly essential for sensitive queries. To balance privacy and utility, a series of lightweight obfuscation approaches has recently been proposed, where users locally transform plaintext embeddings into the fixed ciphertext ones. While such Embedding-to-Embedding Obfuscation (E2EO) schemes demonstrate considerable resilience against traditional token frequency and embedding inversion attacks, the core mechanism behind remains to be the large-scale one-to-one substitution, which provides no cryptographic guarantees. In this paper, we propose Proxy Manifold Alignment (PMA), a novel attack against E2EO in privacy-preserving LLMs. Our key observation is that E2EO schemes keep the original semantic structure, so that the obfuscated vector stream can be regarded as an unknown tokenizer-language whose symbols are the vectors themselves. Therefore, the proposed ciphertext to plaintext reconstruction attack can be formulated as a translation task from the unknown tokenizer-language to plaintext. Specifically, by only accessing the obfuscated vector stream, the target tokenizer and a public corpus, the PMA attack first employs Word2Vec to model the co-occurrence patterns within the obfuscated stream and the public corpus independently, and constructs two proxy vector embeddings. Then, the attack aligns the underlying manifolds of these two embeddings based on structural similarity. Finally, it maps the obfuscated vectors back to plaintext. Experimental results demonstrate that PMA consistently achieves higher plaintext recovery than other state-of-the-art attack methods.

cs.CR↗

arXiv2026

Universal Image Restoration via Internalized Chain-of-Thought Reasoning

Image restoration seeks to recover high-quality images from degraded inputs but becomes highly ill-posed under complex, mixed degradations. While unified all-in-one models are common, their performance declines as degradation complexity increases. Recent works adopt Chain-of-Thought (CoT) reasoning for multi-round restoration using specialized modules. However, this approach faces two key limitations: (i) increased computational cost due to multi-step processing, and (ii) weak modeling of interactions between degradations during stepwise inference. We introduce CoTIR, a universal image restoration framework that internalizes CoT reasoning within a single model. Concretely, we view image restoration as a specialized subtask of image editing, which implies that a large-scale pre-trained editing model provides a more favorable optimization starting point. Building on this, we fine-tune the model for restoration and further encode structured CoT-style reasoning into the learning objective via a differentiable formulation inspired by Lagrangian optimization, enabling holistic restoration without chaining specialized restorers. To facilitate training and evaluation, we further present CoTIR-Bench, a large-scale benchmark comprising 5.2 million samples with CoT-style reasoning traces. Extensive experiments on CoTIR-Bench and broad real composite degradation scenes show that CoTIR achieves stronger perceptual quality and more competitive fidelity than both all-in-one models and multi-round restoration methods. The source code is available at https://github.com/gy65896/CoTIR.

cs.CV↗

arXiv2026

Shatter Throughput Ceilings: Leveraging Reflection Surfaces to Enhance Transmissions for Vehicular Fast Data Exchange

Rapid emergence of smart mobility necessitates high-volume bursty data transmission over a single link between a target vehicle and its designated edge computing-enabled Base Station (BS) or Roadside Unit (RSU), which must be completed within a short time period when the vehicle traverses the coverage area. However, in bandwidth-limited scenarios, conventional communication systems face a fundamental throughput ceiling at each single vehicle. This limitation persists even when all time-frequency resources are allocated to a single vehicle, as the underlying channel lacks sufficient spatial diversity to support higher data rates. To break this throughput ceiling, in this paper, we propose a novel reflection-enhanced transmission framework by strategically employing dedicated specular reflecting surfaces along roadways to proactively augment the transmission environments. This setup concentrates dispersed signals from multiple directions toward a target vehicle, analogous to the light-focusing effect of a concave magnifying lens, thereby enhancing the spatial diversity and achievable rank of an individual channel. This allows a BS to allocate more transmission layers to one single user, consequently significantly raising the throughput ceiling for individual vehicles. Moreover, we also introduce dynamic virtualization methods for reflecting panel patch groups, compatible with existing communication systems, to flexibly manage interference with other coexisting users. Furthermore, collaborative rotation among multiple reflecting panels is introduced to enhance signal concentration. Finally, the schematic effectiveness is rigorously validated through 3GPP-compliant system-level simulations, demonstrating significant throughput boosts.

cs.IT↗

arXiv2025

Yours or Mine? Overwriting Attacks Against Neural Audio Watermarking

As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

cs.CR↗