With the development of network bandwidth and the explosive growth of information,the number of users transmitting images to the public cloud for storage is surging,stimulating the demand for real-time image transmiss...With the development of network bandwidth and the explosive growth of information,the number of users transmitting images to the public cloud for storage is surging,stimulating the demand for real-time image transmission,processing,and secure storage.Processing a large amount of image data takes a lot of software and hardware resources;thus,image compression technology is essential.Simultaneously,protecting the privacy and security of the image during transmission and storage is the key to the introduced application scenario.However,it is hard to meet the real-time requirements by using Single Instruction and Single Data algorithms.To fix the issues,the focus of this work is on the implementation of JPEG compression with NEON technology on the FT-2000/4 platform,alongside the development of a system named IMAGEDETECTIVE.The goal is to address the challenges associated with real-time image transmission,processing,and secure storage.The open-source library libjpeg-turbo is utilized to achieve efficient compression and decompression.To detect image tampering,a convolutional neural network is proposed,taking into consideration the“double compression ratio”characteristic often observed in tampered JPEG figures.Additionally,the FT-2000/4 platform’s hardware SCTO module is employed for JPEG image encryption.In addition,we employ the NEONMATH,a NEON technology math library we proposed,to accelerate the process of JPEG image pre-processing and training phase of the neural network.Our system improves the speed of JPEG compression and decompression by 8.72×,and the accuracy of tampering detection reaches 93.3%.The code is available at https://github.com/nudty uyu/IMAGE-DETEC TIVE.展开更多
基金funded by National Nature Science Foundation of China[Grant number 62032001 and 62203457]National Key Research and Development Programs of China[Grant number 2020AAA0104602]。
文摘With the development of network bandwidth and the explosive growth of information,the number of users transmitting images to the public cloud for storage is surging,stimulating the demand for real-time image transmission,processing,and secure storage.Processing a large amount of image data takes a lot of software and hardware resources;thus,image compression technology is essential.Simultaneously,protecting the privacy and security of the image during transmission and storage is the key to the introduced application scenario.However,it is hard to meet the real-time requirements by using Single Instruction and Single Data algorithms.To fix the issues,the focus of this work is on the implementation of JPEG compression with NEON technology on the FT-2000/4 platform,alongside the development of a system named IMAGEDETECTIVE.The goal is to address the challenges associated with real-time image transmission,processing,and secure storage.The open-source library libjpeg-turbo is utilized to achieve efficient compression and decompression.To detect image tampering,a convolutional neural network is proposed,taking into consideration the“double compression ratio”characteristic often observed in tampered JPEG figures.Additionally,the FT-2000/4 platform’s hardware SCTO module is employed for JPEG image encryption.In addition,we employ the NEONMATH,a NEON technology math library we proposed,to accelerate the process of JPEG image pre-processing and training phase of the neural network.Our system improves the speed of JPEG compression and decompression by 8.72×,and the accuracy of tampering detection reaches 93.3%.The code is available at https://github.com/nudty uyu/IMAGE-DETEC TIVE.
