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 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…

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 demonstrates that current AI watermark removal techniques fail to achieve true forensic stealth, as the removal process often leaves behind detectable signals that distinguish the output fro…

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 proposes a novel binomial multibit LLM watermarking scheme that encodes every bit of a payload at every token position, achieving superior message accuracy and robustness compared to existin…

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 generic, standard model construction for Anamorphic Key Encapsulation Mechanisms (AKEM) that achieves strong IND-CCA security, addressing a major gap in covert communication crypt…

The paper proposes Cert-LAS, a novel certified method for verifying model ownership in text-to-image diffusion models, which is robust against malicious signal removal attacks.

The paper introduces ReTokSync, a self-synchronizing framework that resolves tokenization ambiguity in Generative Linguistic Steganography (GLS) by correcting mismatches only when they occur, thereby…

DeepSignature proposes a novel, cryptographically verifiable watermarking system that uses deep neural networks to embed digital signatures into images, enabling robust source attribution and near 100…

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…

LoRA-Key introduces a user-centric watermarking framework that attaches a recoverable ownership key to LoRA modules via a standalone Watermark LoRA, providing lightweight, plug-and-play copyright prot…

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 scalable, helper-data-free open-set framework using an OpenGAN-based classifier to unify authentication for diverse and large populations of heterogeneous PUF-based IoT devices.

The paper demonstrates that cryptographically undetectable backdoors can be embedded into modern, state-of-the-art neural networks by exploiting inherent, latent geometric properties of the learned re…

The paper establishes a universal, machine-checked 1-Bit Barrier for the internal wire map of masked Barrett reduction, providing a strong side-channel leakage bound for post-quantum cryptography.

MATRIX is a novel, robust code watermarking framework that encodes watermarks using constrained parity-check matrix equations, achieving high detection accuracy and improved robustness for code proven…

The paper proposes a simple, generic attack strategy—re-watermarking—that reliably suppresses existing watermarks, demonstrating that watermarks can be used to attack other watermarks.

The paper proposes SecurePix, a compact CMOS-compatible pixel architecture that achieves true in-pixel encryption using FeFETs, demonstrating strong image security and low power overhead.