The paper characterizes the secure rate-distortion-perception (RDP) trade-off region for neural image compression over various noisy and noiseless channels, demonstrating that randomized distributed f…

This paper proposes a novel data-driven image encryption framework that learns the chaotic map dynamics directly from the image data, enhancing security beyond traditional fixed-map schemes.

The paper proposes a novel, highly secure real-time ECG monitoring framework that uses a patient's own ECG signal characteristics to generate unique, dynamic encryption keys, ensuring confidential dat…

The paper proposes CFE-PPAR, the first compression-friendly encryption method for privacy-preserving action recognition, allowing video transformers to recognize actions directly from compressed, encr…

The paper proposes an adaptive steganographic encryption framework that uses a fuzzy inference system to determine pixel-wise embedding depth based on local image features, thereby improving undetecta…

The paper introduces DiffusionHijack, a supply-chain backdoor attack that compromises the PRNG used by diffusion models to deterministically control generated images, which is successfully mitigated b…

The paper proposes a secure and practical black-box text steganography method that uses a dynamic codebook and a multimodal LLM to embed secret messages into captions, outperforming existing technique…

The paper introduces the base-m length codec, a canonical and robust encoding scheme that maps byte strings to lists of residues modulo m, essential for finite-ring cryptosystems.

This paper provides a comparative analysis and benchmarking of Secure Multi-Party Computation (SMPC) and Fully Homomorphic Encryption (FHE) for machine learning, finding that the optimal choice depend…

The paper proposes a multi-ciphertext privacy-preserving framework to efficiently compute high-resolution image gradients using Fully Homomorphic Encryption (FHE) by dividing the large image into smal…

The paper proposes a compression pipeline combining few-step distillation and low-bit quantization to significantly reduce the deployment cost and parameter footprint of large dual-expert video diffus…

The paper proposes a novel symmetric stream cipher utilizing the chaotic dynamics of random cubic mappings in the complex plane to generate highly random and secure key streams.

This paper introduces a unified threat model and evaluation framework to systematically compare privacy-preserving techniques for distributed learning in IoT systems, highlighting the trade-off betwee…

The paper proposes Triumvir, a multi-modal ensemble architecture that significantly improves the classification of small, raw data fragments to distinguish between encrypted and compressed data, outpe…

The paper proposes using Set Shaping Theory (SST) as a preprocessing layer for LSB steganography, demonstrating that it significantly reduces the statistical detectability of embedded messages without…

The paper proposes an efficient and provably secure linguistic steganography method using range coding that achieves high embedding capacity and speed, outperforming existing methods.

This paper proposes and demonstrates a method to secure high-performance RDMA data transfers by implementing AES-128 encryption directly within a programmable network switch, maintaining high throughp…

The paper introduces SeedHijack, a novel, undetectable supply-chain attack that biases LLM watermarking signals by hijacking the underlying Pseudo-Random Number Generator (PRNG) without altering the g…

The paper introduces SeedHijack, a novel, undetectable supply-chain attack that biases LLM watermarking signals by hijacking the underlying PRNG, thereby amplifying the watermark without altering the…

XMark introduces a novel multi-bit watermarking technique that reliably embeds binary messages into LLM-generated text while maintaining high text quality and robust performance even with limited toke…