Convolutional Neural Network Based Text Steganalysis

Wen, Juan; Zhou, Xuejing; Zhong, Ping; Xue, Yiming

doi:10.1109/lsp.2019.2895286

Cited by 58 publications

(19 citation statements)

References 7 publications

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“…In order to further measure the security of different steganographic algorithms, we use two latest steganalysis methods: LS-CNN [25] and TS-BiRNN [26] to detect the steganographic texts generated by RNN-Stega [14], AC-Stega [17], PD-HC, and PD-AC respectively. Firstly, classified sentences are generated from different corpora under different embedding rates.…”

Section: Anti-steganalysis Abilitymentioning

confidence: 99%

A Generation-based Text Steganography by Maintaining Consistency of Probability Distribution

2021

KSII TIIS

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Text steganography combined with natural language generation has become increasingly popular. The existing methods usually embed secret information in the generated word by controlling the sampling in the process of text generation. A candidate pool will be constructed by greedy strategy, and only the words with high probability will be encoded, which damages the statistical law of the texts and seriously affects the security of steganography. In order to reduce the influence of the candidate pool on the statistical imperceptibility of steganography, we propose a steganography method based on a new sampling strategy. Instead of just consisting of words with high probability, we select words with relatively small difference from the actual sample of the language model to build a candidate pool, thus keeping consistency with the probability distribution of the language model. What's more, we encode the candidate words according to their probability similarity with the target word, which can further maintain the probability distribution. Experimental results show that the proposed method can outperform the state-of-the-art steganographic methods in terms of security performance.

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Section: Anti-steganalysis Abilitymentioning

confidence: 99%

A Generation-based Text Steganography by Maintaining Consistency of Probability Distribution

2021

KSII TIIS

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“…There are a number of papers for detecting steganography performed through embedding [246,261]. The idea is to detect differences in latent spaces and probability distributions between the images or text that contains messages compared to the version with nothing embedded.…”

Section: Detecting Steganographymentioning

confidence: 99%

Generative Models for Security: Attacks, Defenses, and Opportunities

Bauer¹,

Bindschaedler²

2021

Preprint

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Generative models learn the distribution of data from a sample dataset and can then generate new data instances. Recent advances in deep learning has brought forth improvements in generative model architectures, and some state-of-the-art models can (in some cases) produce outputs realistic enough to fool humans.We survey recent research at the intersection of security and privacy and generative models. In particular, we discuss the use of generative models in adversarial machine learning, in helping automate or enhance existing attacks, and as building blocks for defenses in contexts such as intrusion detection, biometrics spoofing, and malware obfuscation. We also describe the use of generative models in diverse applications such as fairness in machine learning, privacy-preserving data synthesis, and steganography. Finally, we discuss new threats due to generative models: the creation of synthetic media such as deepfakes that can be used for disinformation. INTRODUCTIONGenerative models learn to characterize the distribution of data using only samples from it and then generate new data instances from this distribution. Although generative models are not new, the deep learning revolution has reinvigorated research into generative model architectures, and deep generative models using state-of-the-art architectures can now produce output that is sometimes indistinguishable from real-world data. Along with this, comes a host of new issues and opportunities relating to security and privacy.This paper provides a comprehensive survey of research at the intersection of generative models and security and privacy. In particular, we describe the recent use of generative models in adversarial machine learning. We also discuss applications such as producing adversarial examples without perturbations, steganography, and privacy-preserving data synthesis. We show how a number of attacks and defenses for various cybersecurity problems such as password generation, intrusion, and malware detection, can benefit from generative models because of their ability to learn the distribution of the training data. By characterizing the data distribution, generative models help practitioners better understand phenomena they are studying and supplement existing attack and defense techniques. Finally, we discuss the extent to which deep generative models present new threats when used to produce synthetic media.The increased intensity of work at the intersection of generative models and security/privacy is evidenced by a growing body of literature. This is illustrated in Fig. 1, which shows the number of papers (published and pre-prints) on this topic from 2000 to 2020.This survey is structured as follows. Section 2 provides a brief overview of generative model architectures, as well as a discussion of metrics used to evaluate generative models. Section 3, we survey the use of generative models for intrusion detection, malware detection, and biometric spoofing. In Section 4, we describe how generative models can be used to create synthetic dat...

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“…The art and science of Steganalysis [58] aim at detecting and possibly extracting potentially veiled artifacts known as the payload (referred to as active Steganalysis [59]) from pragmatic data either with or without the prior knowledge of the underneath Steganography algorithm and allied parameters. This technique has gained significant prominence in forensic sciences and attained state-level recognition [60] in technically advanced countries [61] because detection or unveiling of the concealed information may help avoid and overcome catastrophic security situations.…”

Section: A Steganalysismentioning

confidence: 99%

Prudently Secure Information Theoretic LSB Steganography for Digital Grayscale Images

Rafat¹

2020

IJACSA

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The endangerment of online data breaches calls for exploring new and enhancing existing sneaky ways of clandestine communication to tailor those to match the present and futuristic technological and environmental needs, to which malicious intruders wouldn't have an answer. Cryptography and Steganography are the two distinct techniques that, for long, have remained priority choices for hiding vital information from the unauthorized. But the visibility of the encrypted contents makes these vulnerable to attack. Also, the recent legislative protection agreed to law enforcement authorities in Australia to sneak into pre-shared cryptographic secret keys (PSKs) shall have a devastating impact on the privacy of the people. Hence, the need of the hour is to veil in the encrypted data underneath the cover of Steganography, whose sole intent is to hide the very existence of information. This research endeavor enhances one of the most famous images Steganography technique called the Least Significant Bit (LSB) Steganography, from the security and information-theoretic standpoint by taking a known-cover and known-message attack scenario. The explicit proclamation of this research endeavor is that the security of LSB Steganography lies in inducing uncertainty at the time of bit embedding process. The test results rendered by the proposed methodology confers on the non-detectability and imperceptibility of the confidential information along with its strong resistance against LSB Steganalysis techniques.

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Convolutional Neural Network Based Text Steganalysis

Cited by 58 publications

References 7 publications

A Generation-based Text Steganography by Maintaining Consistency of Probability Distribution

A Generation-based Text Steganography by Maintaining Consistency of Probability Distribution

Generative Models for Security: Attacks, Defenses, and Opportunities

Prudently Secure Information Theoretic LSB Steganography for Digital Grayscale Images

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