MelShield is a robust, in-generation audio watermarking framework that embeds identifiable signals into AI-generated speech in the Mel-spectrogram domain for reliable copyright protection and attribut…

This paper introduces SSTMark, a training-free speech watermarking framework that encodes watermark information into the semantic content of generated speech.

The paper proposes Asymmetric Phase Coding (APC), a training-free cryptographic audio watermarking scheme that achieves high extraction rates (97.5%-98.3%) across various real-world and adversarial at…

The paper introduces DiffErase, a black-box attack that effectively removes inaudible audio watermarks while preserving perceptual quality by utilizing diffusion models.

This paper proposes a neural audio watermarking method that embeds a message into the continuous latent representation of a codec-like speech autoencoder for improved codec robustness.

Researchers present a self-supervised method for reconstructing typed text from keystroke sounds without labeled data, achieving high accuracy in various scenarios.

This paper introduces Audio BERT (AuB) and SpInv, methods for recovering embeddings from speech tokens and performing speaker inversion attacks using only three seconds of frontend output.

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 AudioHijack, a framework that successfully demonstrates context-agnostic and imperceptible auditory prompt injection attacks, showing that commercial Large Audio-Language Models c…

This paper introduces Speaker Anonymization (SA) as a novel perturbation mechanism for zero-shot voice conversion, balancing timbre leakage and prosodic utility while enabling strictly causal, zero-lo…

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

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…

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 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 a comprehensive application-layer reference monitor to detect and mitigate data exfiltration via covert channels embedded in LLM agent egress payloads across text, image, and audio…

The paper proposes Domain-Aware Sharpness Minimization (DASM), a novel optimizer that enhances the robustness and generalization of voice stream steganalysis models across varying data distributions.

The paper proposes TRIDENT, a framework to restore a source speaker's identity from converted audio using a three-pronged architecture.

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 Noise-Aware BEATs (NABEATs), a noise-aware audio self-supervised learning framework that estimates clean BEATs representations from noisy audio signals using an auxiliary referenc…