Table 1 From Provably Secure Generative Linguistic Steganography Semantic Scholar

Table 1 From Provably Secure Generative Linguistic Steganography | Semantic Scholar

Table 1 From Provably Secure Generative Linguistic Steganography | Semantic Scholar Table 1: results of er, kld1 and kld2. "provably secure generative linguistic steganography". Provably secure generative linguistic steganography. in findings of the association for computational linguistics: acl ijcnlp 2021, pages 3046–3055, online. association for computational linguistics. more options….

Table 1 From Provably Secure Generative Linguistic Steganography | Semantic Scholar

Table 1 From Provably Secure Generative Linguistic Steganography | Semantic Scholar In this paper, to further ensure security, we present a novel provably secure generative linguistic steganographic method adg, which recursively embeds secret information by adaptive dynamic grouping of tokens according to their probability given by an off the shelf language model. Previous works of generative linguistic steganography inevitably introduce distortions to the distribution estimated by off the shelf language models. in this paper, we attempted to achieve provably secure generative linguistic steganography during the procedure of stegotext generation. In this paper, to further ensure security , we present a novel provably secure generative linguistic steganographic method adg, which recursively embeds secret information by adaptive. In this paper, we attempted to achieve provably se cure generative linguistic steganography during the procedure of stegotext generation. we proposed adg, which embeds secret information by adap tive dynamic grouping.

Figure 1 From Provably Secure Disambiguating Neural Linguistic Steganography | Semantic Scholar

Figure 1 From Provably Secure Disambiguating Neural Linguistic Steganography | Semantic Scholar In this paper, to further ensure security , we present a novel provably secure generative linguistic steganographic method adg, which recursively embeds secret information by adaptive. In this paper, we attempted to achieve provably se cure generative linguistic steganography during the procedure of stegotext generation. we proposed adg, which embeds secret information by adap tive dynamic grouping. To sum up, the main contributions of this work are three fold: we introduce vaes, gans and flow based generative models for black box sampling based provably secure steosystems. A novel secure disambiguation method named syncpool is proposed, which effectively addresses the segmentation ambiguity problem and has the potential to significantly improve the reliability and security of neural linguistic steganography systems. The rapid development of generative models and the widespread dissemination of generated data bring new technical means and camouflage environments to the provably secure steganography. In this paper, to further ensure security, we present a novel provably secure generative linguistic steganographic method adg, which recursively embeds secret information by adaptive dynamic grouping of tokens according to their probability given by an off the shelf language model.

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