The paper proposes a provably secure steganography scheme based on list decoding that significantly increases embedding capacity for Large Language Models (LLMs) compared to existing methods.

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

The paper proposes PUSNet-MK, an extension to PUSNet that enables secure multi-user, multi-key image steganography by introducing a mismatched-key isolation loss to prevent cross-key decoding.

The paper introduces SADBench, a systematic benchmark designed to evaluate both the effectiveness of steganographic attacks injecting harmful content and the robustness of steganalysis defenses agains…

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 a theoretically grounded evaluation framework for watermarking generative models, proposing a novel method (SSB) that allows for systematic design across all security-robustness-f…

This paper studies covert communication in a scalar Gaussian model, deriving the maximal reliably transmissible covert payload and establishing first-order optimality.

Quantum Gatekeeper is a robust, multi-factor context-bound image steganography framework that embeds payloads using LSB and derives a gate key from a Variational Quantum Circuit (VQC), ensuring recove…

The paper proposes a steganographic mechanism, analogous to genetic inheritance, to track the lineage of synthetic information within a cyber ecosystem.

This paper proposes a novel Simultaneous Data Compression and Encryption (SDCE) system that combines chaotic map-based encryption with Huffman encoding to securely and efficiently transmit large video…

The paper demonstrates a class of steganographic exfiltration attacks against vector databases by hiding data within embeddings, and proposes VectorPin, a cryptographic provenance protocol to detect s…

The paper introduces 'contrastive privacy,' a formal, model-agnostic, and quantitative method for evaluating the semantic success of AI-based sanitization across multiple media modalities.

This paper introduces HiTMS, a method for concealing secrets in large language models across multiple responses in successive rounds, improving throughput and reducing steganalyzer detection.

The paper introduces a simple, near-optimal detection mechanism for the Gumbel watermarking scheme, proving its effectiveness under i.i.d. next-token sampling.

Gaussian Shannon proposes a novel watermarking framework that treats diffusion generation as a noisy communication channel, enabling both robust tracing and exact bit-level recovery of embedded waterm…

This paper develops provably undetectable and robust watermarking schemes for LLM outputs even when the per-token entropy is only constant, removing previous dependencies on high entropy rates or larg…

The paper proposes W-IR, a novel watermarking framework that simultaneously achieves high certified robustness against adversarial attacks and effectively mitigates identity leakage in watermarked ima…