Conceptos Básicos
Proposing a zero-shot approach for linguistic steganography based on in-context learning to achieve better imperceptibility.
Resumen
Generative linguistic steganography aims to hide secret messages within covertext. Previous studies focused on statistical differences, but ill-formed stegotext can be easily identified. The proposed zero-shot approach uses in-context learning to enhance perceptual and statistical imperceptibility. New metrics and language evaluations are designed to measure stegotext imperceptibility. Experimental results show the method produces more intelligible stegotext than previous methods. Data transmission security is crucial due to potential message alteration by attackers. Steganography conceals sensitive messages within public channels using multimedia carriers like text, image, audio, or video. Secure communication involves embedding secret messages into a stego-carrier controlled by a shared key. Statistical imperceptibility measures the distance between covertext and stegotext distributions, while perceptual imperceptibility lacks clear measurement methods. Modern steganography utilizes machine learning techniques for enhanced imperceptibility. Generation-based steganography techniques include rule-based, statistical, and combined approaches. Recent advancements in machine learning and natural language processing have improved linguistic steganography performance.
Estadísticas
Our method produces 1.926× more innocent and intelligible stegotext than any other method.
The project is available at https://github.com/leonardodalinky/zero-shot-GLS.
JSD(Ptrue ∥ Pstega) ≤ JSD(Ptrue ∥ PLM) + JSD(PLM ∥ Pstega)
Full JSD measures statistical imperceptibility between covertext and stegotext.
Half JSD estimates imperceptibility under different but similar distributions.
Zero JSD evaluates imperceptibility between stegotext and normal text.
Citas
"Data transmission is generally secured using encryption algorithms to create an unrecognizable ciphertext for secure data transmission."
"Steganography is the key to ensuring communication privacy by concealing sensitive messages within monitored channels."
"Our experimental results indicate that our method produces 1.926× more innocent and intelligible stegotext than any other method."