5 research outputs found

    Adaptive Reversible Data Hiding Scheme for Digital Images Based on Histogram Shifting

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    Existing histogram based reversible data hiding schemes use only absolute difference values between the neighboring pixels of a cover image. In these schemes, maxima and minima points at maximum distance are selected in all the blocks of the image which causes shifting of the large number of pixels to embed the secret data. This shifting produces more degradation in the visual quality of the marked image. In this work, the cover image is segmented into blocks, which are classified further into complex and smooth blocks using a threshold value. This threshold value is optimized using firefly algorithm. Simple difference values between the neighboring pixels of complex blocks have been utilized to embed the secret data bits. The closest maxima and minima points in the histogram of the difference blocks are selected so that number of shifted pixels get reduced, which further reduces the distortion in the marked image. Experimental results prove that the proposed scheme has better performance as compared to the existing schemes. The scheme shows minimum distortion and large embedding capacity. Novelty of work is the usage of negative difference values of complex blocks for secret data embedding with the minimal number of pixel shifting

    Cellular Automata Based Image Authentication Scheme Using Extended Visual Cryptography

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    Most of the Visual Cryptography based image authentication schemes hide the share and authentication data into cover images by using an additional data hiding process. This process increases the computational cost of the schemes. Pixel expansion, meaningless shares and use of codebook are other challenges in these schemes. To overcome these issues, an authentication scheme is proposed in which no embedding into the cover images is performed and meaningful authentication shares are created using the watermark and cover images. This makes the scheme completely imperceptible. The watermark can be retrieved just by superimposing these authentication shares, thus reducing the computational complexity at receiver's side. Cellular Automata is used to construct the master share that provides self-construction ability to the shares. The meaningful authentication shares help in enhancing the security of the scheme while size invariance saves transmission and storage cost. The scheme possesses the ability of tamper detection. Experimental results demonstrate the improved security and quality of the generated shares of the proposed scheme as compared to existing schemes

    Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model

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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

    Engineering heterogenous catalysts for chemical CO2 utilization: Lessons from thermal catalysis and advantages of yolk@shell structured nanoreactors

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