Research On JPEG Heavy Compression Detection Of Color Image

As the Internet occupies a more and more important position in all fields of our life and society,a large number of digital images appear in the public view through the Internet platform.These digital images have more or less experienced artificial post-processing,and there are also some products that have been maliciously tampered with by criminals to help them achieve their illegal purposes.Therefore,the field of digital image forensics has been widely concerned.The compression history of digital image can be used as an important basis to detect whether the digital image has been processed.Among them,JPEG re compression detection is the most commonly used and effective field,because many imaging devices will save the image in JPEG format for convenience during imaging,and people generally save the image in JPEG format by default after using many graphics processing software.At this time,JPEG secondary compression is caused.Therefore,detecting whether the image has undergone JPEG re compression can be used as an important basis to judge whether the image has undergone tampering.This paper studies the field of JPEG re compression detection.The work is as follows:(1)The existing double JPEG compression algorithm does not consider the double hybrid compression problem,which may be circumvented by the criminals introducing another compression algorithm such as JPEG2000 during the secondary compression to avoid the existing double JPEG compression detection algorithm.To solve this problem,this paper proposes a double hybrid compression detection algorithm based on quaternion convolutional neural network.The algorithm can not only preserve the relationship between the color image channels,but also make full use of the color information of the color image,and extract the color features of the image through quaternion convolution to assist decision-making.A feature conversion module is added to the network to convert the extracted features into quaternion statistical features to amplify the traces introduced by secondary compression,guide the network to capture the loss of image color information caused by recompression,and enhance the network’s performance.Sensitivity.The experimental results show that the accuracy rate of the proposed method on the double mixed compression detection of JPEG and JPEG2000 is up to 98%,and the performance is improved by an average of 27% compared with the existing double JPEG compression detection algorithm.On this issue,good results were also obtained.Compared with the existing double compression detection methods,the proposed model has better transferability and versatility in both JPEG and double hybrid compression detection problems.(2)Most of the existing deep learning-based synchronous double JPEG compression detection algorithms use the truncation and rounding errors generated in the decompression process as the classification basis.Before the detection framework,there is a preprocessing layer to reduce the difficulty of feature extraction,which cannot achieve end-to-end.At the same time,the existing quantization base table is not necessarily the optimal solution for the decompression process,which limits the upper limit of the precision of the double JPEG compression detection algorithm.In response to these problems,this paper proposes a double JPEG compression detection method based on a decompression module,which uses convolution to simulate the JPEG decompression process,designs a decompression module,and integrates the JPEG decompression process into the network to achieve end-to-end,eliminating the need for heavy preprocessing steps.At the same time,the use of deep learning can automatically optimize the parameters and automatically seek the optimal solution of the decompression process,which reduces the secondary loss of image information caused by manual processing to a certain extent,and further improves the performance limit of the double JPEG compression detection algorithm.The experimental results show that the proposed double JPEG synchronous compression detection algorithm has achieved good forensic performance on multiple public datasets,and on all experimental groups with quality factors ranging from20 to 90,the accuracy of most experimental groups is better than the existing ones.