This paper proposes an image encryption technique which is fast and secure. The encryption scheme is designed for secure transmission of video surveillance data (keyframes) over insecure network. The image encryption technique employs 1D Sine–Sine system with better chaotic properties than its seed map and faster than higher-dimensional chaotic systems. Further, design of encryption scheme is based on two permutation rounds, which employs pixel swapping operation and diffusion operation which is simple and provides required security against plaintext, differential and various other attacks. Three separate chaotic sequences are generated using 1D Sine–Sine system which enhances the key space of the encryption scheme. Secret keys are updated dynamically with SHA-256 hash value obtained from plain image. Hash values of plain image are efficiently used without loss of any hash value information. This makes the encryption scheme plaintext sensitive and secure against plaintext attacks. Performance and security aspects of encryption scheme is analyzed both quantitatively using predefined security metrics and qualitatively by scrutinizing the internal working of encryption scheme. Computational complexity of encrypting a plain image of size [Formula: see text] is [Formula: see text] and is suitable for encrypting keyframes of video for secure surveillance applications.

This paper presents cryptanalysis of a color image encryption scheme. DNA encoding and multiple 1D chaotic maps are used in the encryption process which increases its computational speed. The key streams generated in this scheme are dependent on secret keys, updated using the sum of pixel intensities of plain image of size [Formula: see text]. This paper analyzes the security of encryption scheme against the chosen plaintext attack and finds that only [Formula: see text] different key matrices for diffusion are possible, an equivalent version of which can be revealed with [Formula: see text] chosen plain images. Experimental results are presented to prove that equivalent diffusion keys and block permutation sequence can be effectively revealed through the attack. In addition, low sensitivity of keys towards changes in plaintext along with insecure diffusion process involved in encryption process is also reported. Finally, to remedy the shortcomings of the original encryption scheme, an enhanced encryption scheme is generated that can resist chosen/known plaintext attack while maintaining the merits of the original encryption scheme.

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Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.

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