Abstract
Images are the most venerable form of data in terms of security. Daily around 1.8 billion images are added to the Internet and most of the data on the Internet is public. So, it is desired to make sure that images are secure before transmitting through an insecure network like the Internet. Various techniques are used for the protection of these digital images like steganography, watermarking, and encryption. These methods are used for achieving security goals like confidentiality, integrity, and availability. Individually none of the methods can achieve all security goals, so, this paper presents you a blended approach of the above-mentioned methods. A modern approach of using artificial neural network is proposed in this paper. In ANN, we will focus particularly on convolutional neural network (CNN). Since CNN works on the pixels of the image and deals with the subject like object detection, face recognition, so, it will enhance the current methods in terms of efficiency, accuracy, and computational power and will improve the security of image. The method presented in this paper is efficient and secured against the attacks and risks related to data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ulutas, M., Ulutas, G., Nabiyev, V.V.: Medical image security and EPR hiding using Shamir’s secret sharing scheme. J. Syst. Softw. 84(3), 341–353 (2011)
Schmidt, T.: A Review of Applications of Artificial Neural Networks in Cryptosystems. Department of Computer Science, Ryerson University, Canada
Petkovic, M., Jonker, W.: Preface, special issue on secure data management. J. Comput. Secur. 17(1), 1–3 (2009)
Xiang, Z., et al.: A new convolutional neural network-based steganalysis method for content-adaptive image steganography in the spatial domain. IEEE Access 8, 47013–47020 (2020)
Razzaq, M.A., et al.: Digital image security: fusion of encryption, steganography and watermarking. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8(5), (2017)
Saini, J.K., Verma, H.K.: A hybrid approach for image security by combining encryption and steganography. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013). IEEE (2013)
Nezhadarya, E., Wang, Z.J., Ward, R.K.: A Robust Image Watermarking Based on Multiscale Gradient Direction Quantization. IEEE Trans. Inf. Forensics Secur. 6(4), 1200–1213 (2011)
Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996)
-Maniyath, S.R., Thanikaiselvan, V.: An efficient image encryption using deep neural network and chaotic map. Microprocess. Microsyst. 103134 (2020)
Haribabu, K., Subrahmanyam, G.R.K.S., Mishra, D.: A robust digital image watermarking technique using autoencoder based convolutional neural networks. In: 2015 IEEE Workshop on Computational Intelligence: Theories, Applications, and Future Directions (WCI). IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Seem, A., Chauhan, A.K., Khan, R. (2022). Artificial Neural Network, Convolutional Neural Network Visualization, and Image Security. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_51
Download citation
DOI: https://doi.org/10.1007/978-981-16-1740-9_51
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1739-3
Online ISBN: 978-981-16-1740-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)