Adaptive image steganography algorithms have emerged as a means of concealing secret messages within image carriers,employing a manual design of distortion costs to guide the process of message embedding.The primary objective of these algorithms has been to hide secret information in regions of the image that possess intricate and complex textures,thereby thwarting feature-based steganalysis detection methods.However,the rapid advancements in deep learning within the field of steganalysis have posed significant challenges to the efficacy of manually designed adaptive algorithms.Furthermore,there is a pressing need to address the statistical anomalies that arise from the aggregation of complex textures towards the boundaries when employing additive distortion-based steganographic encoding techniques.To tackle these challenges,this paper provides a general summary of the strengths and limitations associated with various handcraft distortion cost design.It also presents an paradigm of the existing design paradigms for adaptive algorithms in the spatial domain,considering the transformation rules of UNI WARD across different embedding domains.In order to improve upon the existing techniques,the paper proposes a universal domain steganographic transformation formula based on the embedding distortion cost p.This formula provides a flexible framework that can be applied to a wide range of mainstream algorithms,enhancing their performance and adaptability.Moreover,this paper introduces a groundbreaking universal domain steganographic algorithm known as Canny Gauss,which capitalizes on multiple techniques to achieve highly effective message embedding.Firstly,the algorithm employs the Canny operator to perform texture segmentation,enabling the identifi-cation and selection of regions within the image that possess rich texture information suitable for embedding secret messages.By leveraging this approach,Canny Gauss ensures that the embedded messages are strategically placed within areas that can effectively camouflage the hidden information.In addition,the algorithm utilizes Gaussian blur to scale the contours of the image.This step is crucial in guaranteeing a seamless integration of the embedded messages with the surrounding textures,making them inconspicuous to visual inspection and steganalysis techniques.To further optimize the performance of the algorithm,an AutoML framework is employed to automatically search for suitable threshold values.This technique enhances the overall robustness and effectiveness of the steganographic process by dynamically adjusting the thresholds based on the characteristics of the input image.By adapting the thresholds to each specific image,Canny Gauss maximizes the concealment of secret messages while minimizing any adverse effects on image quality or detectability.Experimental results demonstrate the efficacy of the proposed universal domain steganographic transformation formula when applied to existing algorithms.In comparison to UNIWARD,the algorithm presented in this paper exhibits improved stability in embedding distortion costs and enhanced steganographic security.Moreover,when coupled with third-party weights,the algorithm showcases notable improvements in deep steganalysis performance,with a minimum enhancement of 2.6％and a maximum enhancement of 14.6％compared to UNI WARD.This paper not only provides valuable insights for the design of adaptive steganography algorithms in universal domains but also offers a new strategie to counter deep steganalysis detection techniques that rely on texture features.
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