Chaos-based Image/Signal Encryption

Últimos artículos

• Implementing a White-Box Undetectable Backdoor for Random Fourier Features

  Michael Collins, Jada Cumberland, Brianne Dunn, Ross Gore, Samuel Jackson, Sachin Shetty · 16 de septiembre de 2026

  Goldwasser et al. showed that undetectable backdoors can be planted in machine learning models trained with the Random Fourier Features (RFF) algorithm, under a hardness assumption tied to the Continuous Learning With Errors (CLWE) problem. Under standard cryptographic assumptions, even a full white…

• Joint Transcription and Decryption of Images of Encrypted Handwritten Documents: A Comparison with the Traditional Pipeline

  Marino Oliveros-Blanco, Lei Kang, Alicia Forn\'es, Be\'ata Megyesi · 29 de junio de 2026

  Historical encrypted manuscripts present a challenging problem at the intersection of cryptology, linguistics, paleography, and computer vision. Current automatic decipherment approaches usually rely on a two-stage pipeline: transcription of cipher symbols from manuscript images, followed by decrypt…

• Homomorphic Encryptions for Privacy Preserving Vision

  Preey Shah, Rohan Virani, Sanjari Srivastava · 25 de junio de 2026

  Legal requirements might prevent organizations from sharing sensitive data like medical or financial details of consumers which prevents them from leveraging cloud based ML-as-a-service solutions provided by third party providers, which are quickly gaining popularity these days. In this project, we …

• TreeGRNG: Binary Tree Gaussian Random Number Generator for Efficient Probabilistic AI Hardware

  Jonas Crols, Guilherme Paim, Shirui Zhao, Marian Verhelst · 16 de junio de 2026

  Bayesian Neural Networks (BNNs) offer opportunities for greatly enhancing the trustworthiness of conventional neural networks by monitoring the uncertainties in decision-making. A significant drawback for BNN inference at the extreme edge, however, is the imperative need to incorporate Gaussian Rand…

• Cryptographic Backdoor for Neural Networks: Boon and Bane

  Anh Tu Ngo, Anupam Chattopadhyay, Subhamoy Maitra · 9 de junio de 2026

  In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the attacks as well as in presenting the defenses as well. On the attack side, a carefully planted cryptographic backdoor enables powerful and invisible attack on th…

• DiffusionHijack: Supply-Chain PRNG Backdoor Attack on Diffusion Models and Quantum Random Number Defense

  Ziyang You, Liling Zheng, Xiaoke Yang, Xuxing Lu · 14 de mayo de 2026

  Diffusion models depend on pseudo-random number generators (PRNGs) for latent noise sampling. We present DiffusionHijack, a supply-chain backdoor attack that hijacks the PRNG to deterministically control generated images. A malicious PRNG, injected via compromised packages, forces pixel-perfect repr…

• Seed Hijacking of LLM Sampling and Quantum Random Number Defense

  Ziyang You, Xiaoke Yang, Zhanling Fan, Feng Guo, Xiaogen Zhou, Xuxing Lu · 12 de mayo de 2026

  Large language models (LLMs) rely on deterministic pseudorandom number generators (PRNGs) for autoregressive sampling, creating a critical supply-chain attack surface overlooked by existing defenses. We present SeedHijack, a backdoor attack that manipulates PRNG outputs to force attacker-specified t…

• Privacy-Preserving Semantic Segmentation without Key Management

  Mare Hirose, Shoko Imaizumi, Hitoshi Kiya · 17 de abril de 2026

  This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed method, the model creator and each client encrypt images using locally generated keys, and model training and inference are conducted on the encrypted …

• Memory Backdoor Attacks on Neural Networks

  Eden Luzon, Guy Amit, Roy Weiss, Torsten Kraub, Alexandra Dmitrienko, Yisroel Mirsky · 19 de diciembre de 2025

  Neural networks are often trained on proprietary datasets, making them attractive attack targets. We present a novel dataset extraction method leveraging an innovative training time backdoor attack, allowing a malicious federated learning server to systematically and deterministically extract comple…

• Safeguarding Privacy in Edge Speech Understanding with Tiny Foundation Models

  Afsara Benazir, Felix Xiaozhu Lin · 2 de diciembre de 2025

  Robust speech recognition systems rely on cloud service providers for inference. It needs to ensure that an untrustworthy provider cannot deduce the sensitive content in speech. Sanitization can be done on speech content keeping in mind that it has to avoid compromising transcription accuracy. Reali…