IJCNIS Vol. 11, No. 11, 8 Nov. 2019
Cover page and Table of Contents: PDF (size: 385KB)
Full Text (PDF, 385KB), PP.21-27
Views: 0 Downloads: 0
Steganography, Security, Copyright, Privacy, Images
The advance of Big Data and Internet growth has driven the need for more abundant storage to hold and share data. People are sending more messages to one another and paying attention to the aspects of privacy and security as opposed to previous decades. One of the types of files that are widely shared and instantaneous available over the web are images. They can become available as soon as a shot is taken and keep this closely related to the owner; it is not easy. It has been proposed here to use Steganography to embed information of the author, image description, license of usage and any other secrete information related to it. Thinking of this, an analysis of the best file types, considering capacity, detectability, and distortion was necessary to determine the best solution to tackle current algorithm weaknesses. The performance of BMP, GIF, and JPEG initialises the process of addressing current weaknesses of Steganographic algorithms. The main weaknesses are capacity, detectability and distortion to secure copyright images. Distributed Steganography technique also plays a crucial part in this experiment. It enhances all the file formats analysed. It provided better capacity and less detectability and distortion, especially with BMP. BMP has found to be the better image file format. The unique combination of Distributed Steganography and the use of the best file format approach to address the weaknesses of previous algorithms, especially increasing the capacity. It will undoubtedly be beneficial for the day to day user of social media image creators and artists looking to protect their work with copyright.
Istteffanny I. Araujo, Hassan Kazemian, "Enhancement of Capacity, Detectability and Distortion of BMP, GIF and JPEG images with Distributed Steganography", International Journal of Computer Network and Information Security(IJCNIS), Vol.11, No.11, pp.21-27, 2019. DOI:10.5815/ijcnis.2019.11.03
[1]Pal, S.And Mishra, S.. (2019). An efficient steganography technique for images using chaotic bitstream. IJCNIS. 10 (1), p45-50. in press. doi: 10.5815/ijcnis.2019.02.03
[2]Budoor, S., Daniyal, A.And Li, C. (2016). Secret communication on facebook using image steganography: experimental study. International Journal of Computer Science and Information Security (IJCSIS). 14 (1), p428-444. in press.
[3]El-Latif, E., Taha, A. And Zayed, H. (2019). A passive approach for detecting image splicing using deep learning and haar wavelet transform. IJCNIS. 5 (1), p28-35. in press. doi: 10.5815/ijcnis.2019.05.04
[4]Hosam, O. (2019). Attacking image watermarking and steganography - a survey. IJCNIS. 3 (1), p23-37. in press. doi: 10.5815/ijitcs.2019.03.03
[5]Douglas, M., Bailey, K., Leeney, M. et al. Multimed Tools Appl (2018). An overview of steganography techniques applied to the protection of biometric data, p77. in press. doi: https://doi.org/10.1007/s11042-017-5308-3
[6]Nag, A. (2019). Low-tech steganography for covert operations. IJCNIS. 2 (1), p21-27. in press.
[7]Kim, C. & Yang, CN. Multimed Tools Appl (2015). Watermark with dsa signature using predictive coding, p74: 5189. in press. doi 10.1007/s11042-013-1667-6
[8]Khami, M.H. (2018). Transmitting security enforcement by text encrypting and image hiding technique using combined encrypt/hide keys. IJEM. 1 (1), p1-15. in press. doi: 10.5815/ijem.2018.01.01
[9]Lin, Chia-Chen & Shiu, Pei-Feng. (2010). High capacity data hiding scheme for dct-based images. Journal of Information Hiding and Multimedia Signal Processing. 1. in press.
[10]Mao, J. et al. (2016). A method to estimate the steganographic capacity in dct domain based on mcuu model. Wuhan University Journal of Natural Sciences. 21 (4), p283–290. in press. doi: https://doi.org/10.1007/s11859-016-1172-7
[11]Mazurczyk, W., KaraĆ, M., Szczypiorski, K. et al (2016) YouSkyde: Information hiding for skype video traffic. Multimed Tools Appl, 75: 13521. in press. doi: https://doi.org/10.1007/s11042-015-2740-0
[12]Mazurczyk, W., Szaga, P. & Szczypiorski, K. (2014). Using transcoding for hidden communication in telephony. Multimed Tools Appl, p70: 2139. in press. doi: https://doi.org/10.1007/s11042-012-1224-8
[13]M. Devi and N. Sharma, Improved detection of least significant bit steganography algorithms in colour and grayscale images. 2014 Recent Advances in Engineering and Computational Sciences (RAECS), Chandigarh, 2014, pp. 1-5. in press. doi: 10.1109/raecs.2014.6799507
[14]Rabie, T. And Kamel, I. (2017). High-capacity steganography: a global-adaptive-region discrete cosine transform approach. Multimedia Tools and Applications. 76 (5), p6473–6493. in press. doi: https://doi.org/10.1007/s11042-016-3301-x
[15]SentinelOne. (2019). Hiding code inside images: how malware uses steganography. Available: https://www.sentinelone.com/blog/hiding-code-inside-images-malware-steganography/. Last accessed 06/10/2019. Unpublished.
[16]Ansari, A. et All. (2019). A comparative study of recent steganography techniques for multiple image formats. IJCNIS. 1 (1), p11-25. in press. doi: 10.5815/ijcnis.2019.01.02
[17]J. Bhatia and M. Okade, A novel image enhancement technique based on statistical analysis of dct coefficients for jpeg compressed images. (2016) Twenty-Second National Conference on Communication (NCC), Guwahati, 2016, pp. 1-6., in press. doi: 10.1109/NCC.2016.7561122