Visible to the public An Adaptive Edge-Based Steganography Algorithm for Hiding Text into Images

TitleAn Adaptive Edge-Based Steganography Algorithm for Hiding Text into Images
Publication TypeConference Paper
Year of Publication2021
AuthorsSarrafpour, Bahman A. Sassani, Alomirah, Reem A., Sarrafpour, Soshian, Sharifzadeh, Hamid
Conference Name2021 IEEE 19th International Conference on Embedded and Ubiquitous Computing (EUC)
KeywordsColor Channel, composability, cyber security, edge detection, filtering algorithms, Image edge detection, Media, Metrics, privacy, pubcrawl, statistical attacks, steganalysis, steganography, steganography detection, Streaming media, ubiquitous computing, visualization
AbstractSteganography is one of the techniques for secure transformation of data which aims at hiding information inside other media in such a way that no one will notice. The cover media that can accommodate secret information include text, audio, image, and video. Images are the most popular covering media in steganography, due to the fact that, they are heavily used in daily applications and have high redundancy in representation. In this paper, we propose an adaptive steganography algorithm for hiding information in RGB images. To minimize visual perceptible distortion, the proposed algorithm uses edge pixels for embedding data. It detects the edge pixels in the image using the Sobel filter. Then, the message is embedded into the LSBs of the blue channel of the edge pixels. To resist statistical attacks, the distribution of the blue channel of the edge pixels is used when embedding data in the cover image. The experimental results showed that the algorithm offers high capacity for hiding data in cover images; it does not distort the quality of the stego image; it is robust enough against statistical attacks; and its execution time is short enough for online data transfer. Also, the results showed that the proposed algorithm outperforms similar approaches in all evaluation metrics.
DOI10.1109/EUC53437.2021.00024
Citation Keysarrafpour_adaptive_2021