Image scrambling adversarial autoencoder based on the asymmetric encryption

Bao, Zhenjie; Ren, Xue; Jin, Yadong

doi:10.1007/s11042-021-11043-3

Cited by 16 publications

(1 citation statement)

References 56 publications

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“…In addition, a neural network is projected to gain the interested object from the ciphertext image. Bao et al 129 constructed an encoder-decoder and discriminator framework trained on the Corel-1000 dataset 130 to imitate the process of image scrambling and reconstruction in which the parameters of the encoder and decoder are different. However, the cipher pixels were not uniformly distributed, the decrypted images quality and the generalization ability of model were not good, and the plaintext and ciphertext image sensitivities were weak.…”

Section: End-to-end Image Encryptionmentioning

confidence: 99%

Survey on deep learning applications in digital image security

Bao

Ren

2021

Opt. Eng.

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In the digital era, sharing pictures on social media has become a common privacy issue. To prevent private images from being eavesdropped on and destroyed, developing secure and efficient image steganography, image cryptography, and image authentication has been difficult. Deep learning provides a solution for digital image security. First, we make an overall conclusion on deep learning applications in image steganography to generate five aspects: the cover image, stego-image, embedding change probabilities, coverless steganography, and steganalysis. Second, we also combine and compare deep learning methods used in six aspects: image cryptography from image compression, image resolution improvement, image object detection and classification, key generation, end-to-end image encryption, and image cryptoanalysis. Third, we collect deep learning methods in image authentication from five perspectives: image forgery detection, watermarked image generation, image watermark extraction and detection, image watermarking attack, and image watermark removal. Finally, we summarize future research directions of deep learning utilization in image steganography, image cryptography, and image authentication.

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Section: End-to-end Image Encryptionmentioning

confidence: 99%