In this paper, a novel property of EtC images is considered under the use of z-score normalization. Accordingly, we propose using EtC images, which have been proposed for EtC systems with JPEG compression. However, there are serious issues with it for end users, due to semi-trusted cloud providers. Using machine learning algorithms in cloud environments has been spreading in many fields. In this paper, we propose a novel privacy-preserving machine learning scheme with encrypted images, called EtC (Encryption-then-Compression) images. Moreover, the decryption of the recompressed image produces an image with a very similar visual quality when compared to the original image, according to the obtained results. This method is efficient to recompress JPEG crypto-compressed images in terms of ratio compression.
Recompress all frames code#
Indeed, using the proposed method in this paper, each recompression can be done directly on the JPEG bitstream by removing the last bit of the code representation of each non-zero coefficient, adapting the entropic code part, and slightly modifying the quantization table. In this work, we present a JPEG crypto-compression method which allows us to recompress a JPEG crypto-compressed image several times, without any information about the secret key or the original image content. However, image processing in the encrypted domain is not a trivial task.
Selective encryption is an efficient tool to mask image content and to protect confidentiality while remaining format-compliant. In this context, the security of the transmission channel and/or the cloud storage can be susceptible to privacy leaks. The rising popularity of social networks and cloud computing has greatly increased number of JPEG compressed image exchanges.
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