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With growing concerns for online privacy and secure data transfer, steganography has evolved as a key technique for concealing sensitive information within normal digital images. This paper introduces StegoSuite, an interactive, web-based environment to improve and streamline picture steganography and steganalysis. The toolkit has numerous modern embedding methods, including Least Significant Bit (LSB) Matching, Discrete Cosine Transform (DCT), Pixel Value Differencing (PVD), and Edge Region Difference Expansion (ERDE), allowing users to select appropriate methods for secure transmission. Another key aspect of StegoSuite is its automatic decoder, utilizing machine learning to identify the adopted steganographic method and retrieve hidden data without any operator assistance. Experimental verification, based on performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Bit Error Rate (BER), supports the accuracy and reliability of the system. StegoSuite dramatically enhances privacy-guaranteeing communications and digital forensic investigations by combining message embedding and forensic analysis into a user-friendly platform.

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Comparative Analysis of Steganographic Techniques Using Online Encoding and Decoding Framework

  • 会议论文

  • 发表日期：

    2026年

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  • Devansh Goel,
  • Juhi Singh,
  • Arun Kumr Singh

摘要

With growing concerns for online privacy and secure data transfer, steganography has evolved as a key technique for concealing sensitive information within normal digital images. This paper introduces StegoSuite, an interactive, web-based environment to improve and streamline picture steganography and steganalysis. The toolkit has numerous modern embedding methods, including Least Significant Bit (LSB) Matching, Discrete Cosine Transform (DCT), Pixel Value Differencing (PVD), and Edge Region Difference Expansion (ERDE), allowing users to select appropriate methods for secure transmission. Another key aspect of StegoSuite is its automatic decoder, utilizing machine learning to identify the adopted steganographic method and retrieve hidden data without any operator assistance. Experimental verification, based on performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Bit Error Rate (BER), supports the accuracy and reliability of the system. StegoSuite dramatically enhances privacy-guaranteeing communications and digital forensic investigations by combining message embedding and forensic analysis into a user-friendly platform.