A New Video Steganography Scheme Based on Shi-Tomasi Corner Detector

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Mstafa, Ramadhan J., Younis, Younis M., Hussein, Haval I., Atto, Muhsin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arnold's Cat Map; Cats; Corner Detector; Cryptography; Detectors; Digital media; Embedding; Embedding Capacity; Imperceptibility; Internet; Multimedia; Noise; Noise levels; Random noise; Robustness; Security; Signal processing algorithms; Signal to noise ratio; Steganography; Transforms; Video Steganography; Visual signals
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3021356

Recent developments in the speed of the Internet and information technology have made the rapid exchange of multimedia information possible. However, these developments in technology lead to violations of information security and private information. Digital steganography provides the ability to protect private information that has become essential in the current Internet age. Among all digital media, digital video has become of interest to many researchers due to its high capacity for hiding sensitive data. Numerous video steganography methods have recently been proposed to prevent secret data from being stolen. Nevertheless, these methods have multiple issues related to visual imperceptibly, robustness, and embedding capacity. To tackle these issues, this paper proposes a new approach to video steganography based on the corner point principle and LSBs algorithm. The proposed method first uses Shi-Tomasi algorithm to detect regions of corner points within the cover video frames. Then, it uses 4-LSBs algorithm to hide confidential data inside the identified corner points. Besides, before the embedding process, the proposed method encrypts confidential data using Arnold's cat map method to boost the security level. Experimental results revealed that the proposed method is highly secure and highly invisible, in addition to its satisfactory robustness against Salt & Pepper noise, Speckle noise, and Gaussian noise attacks, which has an average Structural Similarity Index (SSIM) of more than 0.81. Moreover, the results showed that the proposed method outperforms state-of-the-art methods in terms of visual imperceptibility, which offers excellent peak signal-to-noise ratio (PSNR) of average 60.7 dB, maintaining excellent embedding capacity.