Generative Text Steganography With Large Language Model Ai Research Paper Details
Generative AI With Large Language Models | PDF
Generative AI With Large Language Models | PDF View a pdf of the paper titled generative text steganography with large language model, by jiaxuan wu and 4 other authors. In this paper, we propose a steganography scheme that generates conversational text with specified topics and grammars based on the llms. the algorithm maps a secret as prompts input into the llms to generate steganographic text.
A Comprehensive Guide To Popular Generative AI Text Models | PDF | Artificial Intelligence ...
A Comprehensive Guide To Popular Generative AI Text Models | PDF | Artificial Intelligence ... The paper focuses on a technique called generative text steganography, which uses advanced language models to hide secret messages within normal looking text. the researchers explore ways to build stealthy, black box text generators that can embed hidden information without being discovered. Abstract: linguistic steganography (ls) tasks aim to generate steganographic text (stego) based on secret. only authorized receivers can perceive and extract secrets, thereby protecting privacy. This disclosure describes steganography techniques that leverage machine learning techniques, specifically generative pre trained transformer (gpt) type large language model (llm) to generate human readable natural language text that seamlessly conceals a different message within the text. To address the abovementioned issues, this article proposes a novel text steganography based on long readable text generation. it first determines the topic of the stego text according to the scenarios of the communication parties.
Generative Text Steganography With Large Language Model
Generative Text Steganography With Large Language Model This disclosure describes steganography techniques that leverage machine learning techniques, specifically generative pre trained transformer (gpt) type large language model (llm) to generate human readable natural language text that seamlessly conceals a different message within the text. To address the abovementioned issues, this article proposes a novel text steganography based on long readable text generation. it first determines the topic of the stego text according to the scenarios of the communication parties. A novel linguistic steganographic model based on adaptive probability distribution and generative adversarial network is proposed, which achieves the goal of hiding secret messages in the generated text while guaranteeing high security performance. In this paper, we explore. user interfaces of large language models, which is called llm stega. using the user interfaces of llms. specifically, we first construct a. to embed secret messages. furthermore, to guarantee accurate ex posed. comprehensive experiments demonstrate that the proposed. In this paper, we attempted to use the user inter faces of large language models for generative linguistic steganog raphy. firstly, an encrypted steganographic mapping is proposed to map the secret messages into the words of four keyword sets. Generative ai and large language models (llms) change this dynamic by enabling large scale, sophisticated interpretation of text. they can capture figurative language, implications, connotations, and creative expressions, leading to deeper insights and more consistent classification across large volumes of text.
 have blurred the boundary of high-quality text generation between humans and machines%2C which is favorable for generative text steganography. While%2C current advanced steganographic mapping is not suitable for LLMs since most users are restricted to accessing only the black-box API or user interface of the LLMs%2C thereby lacking access to the training vocabulary and its sampling probabilities. In this paper%2C we explore a black-box generative text steganographic method based on the user interfaces of large language models%2C which is called LLM-Stega. The main goal of LLM-Stega is that the secure covert communication between Alice (sender) and Bob (receiver) is conducted by using the user interfaces of LLMs. Specifically%2C We first construct a keyword set and design a new encrypted steganographic mapping to embed secret messages. Furthermore%2C to guarantee accurate extraction of secret messages and rich semantics of generated stego texts%2C an optimization mechanism based on reject sampling is proposed. Comprehensive experiments demonstrate that the proposed LLM-Stega outperforms current state-of-the-art methods.?resize=650,400 "Generative Text Steganography With Large Language Model | AI Research Paper Details")
Generative Text Steganography With Large Language Model | AI Research Paper Details
Generative Text Steganography With Large Language Model | AI Research Paper Details A novel linguistic steganographic model based on adaptive probability distribution and generative adversarial network is proposed, which achieves the goal of hiding secret messages in the generated text while guaranteeing high security performance. In this paper, we explore. user interfaces of large language models, which is called llm stega. using the user interfaces of llms. specifically, we first construct a. to embed secret messages. furthermore, to guarantee accurate ex posed. comprehensive experiments demonstrate that the proposed. In this paper, we attempted to use the user inter faces of large language models for generative linguistic steganog raphy. firstly, an encrypted steganographic mapping is proposed to map the secret messages into the words of four keyword sets. Generative ai and large language models (llms) change this dynamic by enabling large scale, sophisticated interpretation of text. they can capture figurative language, implications, connotations, and creative expressions, leading to deeper insights and more consistent classification across large volumes of text.
Generative Text Steganography With Large Language Model | AI Research Paper Details
Generative Text Steganography With Large Language Model | AI Research Paper Details In this paper, we attempted to use the user inter faces of large language models for generative linguistic steganog raphy. firstly, an encrypted steganographic mapping is proposed to map the secret messages into the words of four keyword sets. Generative ai and large language models (llms) change this dynamic by enabling large scale, sophisticated interpretation of text. they can capture figurative language, implications, connotations, and creative expressions, leading to deeper insights and more consistent classification across large volumes of text.
Generative Text Steganography With Large Language Model | AI Research Paper Details
Generative Text Steganography With Large Language Model | AI Research Paper Details
How Large Language Models Work
How Large Language Models Work
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 have blurred the boundary of high-quality text generation between humans and machines%2C which is favorable for generative text steganography. While%2C current advanced steganographic mapping is not suitable for LLMs since most users are restricted to accessing only the black-box API or user interface of the LLMs%2C thereby lacking access to the training vocabulary and its sampling probabilities. In this paper%2C we explore a black-box generative text steganographic method based on the user interfaces of large language models%2C which is called LLM-Stega. The main goal of LLM-Stega is that the secure covert communication between Alice (sender) and Bob (receiver) is conducted by using the user interfaces of LLMs. Specifically%2C We first construct a keyword set and design a new encrypted steganographic mapping to embed secret messages. Furthermore%2C to guarantee accurate extraction of secret messages and rich semantics of generated stego texts%2C an optimization mechanism based on reject sampling is proposed. Comprehensive experiments demonstrate that the proposed LLM-Stega outperforms current state-of-the-art methods.?resize=91,91 "Generative Text Steganography With Large Language Model | AI Research Paper Details")
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