• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1359-1368
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• 2021

Steganography is a science of embedding secret data into innocent data and its goal is to conceal the existence of a carrier data. Many research on Steganography has been proposed by various hiding and detection techniques that are based on different algorithms. On the other hand, very few studies have been conducted to analyze the performance of each Steganography software. This paper describes five different Steganography software, each having its own algorithms, and analyzes the difference of each inherent feature. Image quality metrics of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) are used to define its performance of each Steganography software. We extracted PSNR and SSIM results of a quantitative amount of embedded output images for those five Steganography software. The results will show the optimal steganography software based on the evaluation metrics and ultimately contribute to forensics.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.315-328
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• 2019

Image steganalysis is an algorithm that classifies input images into stego images with steganography methods and cover images without steganography methods. Previously, handcrafted feature-based steganalysis methods have been mainly studied. However, CNN-based objects recognition has achieved great successes and CNN-based steganalysis is actively studied recently. Unlike object recognition, CNN-based steganalysis requires preprocessing filters to discriminate the subtle difference between cover images from stego images. Therefore, CNN-based steganalysis studies have focused on developing effective preprocessing filters as well as network structures. In this paper, we compare previous studies in same experimental conditions, and based on the results, we analy ze the performance variation caused by the differences in preprocessing filter and network structure.