In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the imag...In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the image to its original quality.The challenge lies in regenerating significantly compressed images into a state in which these become identifiable.Therefore,this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks(GAN).The generator in this network is based on theU-Net architecture.It features a newhourglass structure that preserves the characteristics of the deep layers.In addition,the network incorporates two loss functions to generate natural and high-quality images:Low Frequency(LF)loss and High Frequency(HF)loss.HF loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features.This can enhance the performance in the high-frequency region.In contrast,LF loss is used to handle the low-frequency region.The two loss functions facilitate the generation of images by the generator,which can mislead the discriminator while accurately generating high-and low-frequency regions.Consequently,by removing the blocking effects frommaximum lossy compressed images,images inwhich identities could be recognized are generated.This study represents a significant improvement over previous research in terms of the image resolution performance.展开更多
This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image...This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.展开更多
Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompress...Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.展开更多
Data hiding(DH)is an important technology for securely transmitting secret data in networks,and has increasing become a research hotspot throughout the world.However,for Joint photographic experts group(JPEG)images,it...Data hiding(DH)is an important technology for securely transmitting secret data in networks,and has increasing become a research hotspot throughout the world.However,for Joint photographic experts group(JPEG)images,it is difficult to balance the contradiction among embedded capacity,visual quality and the file size increment in existing data hiding schemes.Thus,to deal with this problem,a high-imperceptibility data hiding for JPEG images is proposed based on direction modification.First,this proposed scheme sorts all of the quantized discrete cosine transform(DCT)block in ascending order according to the number of non-consecutive-zero alternating current(AC)coefficients.Then it selects non-consecutive-zero AC coefficients with absolute values less than or equal to 1 at the same frequency position in two adjacent blocks for pairing.Finally,the 2-bit secret data can be embedded into a coefficient-pair by using the filled reference matrix and the designed direction modification rules.The experiment was conducted on 5 standard test images and 1000 images of BOSSbase dataset,respectively.The experimental results showed that the visual quality of the proposed scheme was improved by 1∼4 dB compared with the comparison schemes,and the file size increment was reduced at most to 15%of the comparison schemes.展开更多
基金supported by the Technology Development Program(S3344882)funded by the Ministry of SMEs and Startups(MSS,Korea).
文摘In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the image to its original quality.The challenge lies in regenerating significantly compressed images into a state in which these become identifiable.Therefore,this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks(GAN).The generator in this network is based on theU-Net architecture.It features a newhourglass structure that preserves the characteristics of the deep layers.In addition,the network incorporates two loss functions to generate natural and high-quality images:Low Frequency(LF)loss and High Frequency(HF)loss.HF loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features.This can enhance the performance in the high-frequency region.In contrast,LF loss is used to handle the low-frequency region.The two loss functions facilitate the generation of images by the generator,which can mislead the discriminator while accurately generating high-and low-frequency regions.Consequently,by removing the blocking effects frommaximum lossy compressed images,images inwhich identities could be recognized are generated.This study represents a significant improvement over previous research in terms of the image resolution performance.
文摘This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.
基金Supported by the Fundamental Research Funds for the Central Universities (No.500421126)。
文摘Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.
基金supported by the National Natural Science Foundation of China (62072325)Shanxi Scholarship Council of China (HGKY2019081)+1 种基金Fundamental Research Program of Shanxi Province (202103021224272)TYUST SRIF (20212039).
文摘Data hiding(DH)is an important technology for securely transmitting secret data in networks,and has increasing become a research hotspot throughout the world.However,for Joint photographic experts group(JPEG)images,it is difficult to balance the contradiction among embedded capacity,visual quality and the file size increment in existing data hiding schemes.Thus,to deal with this problem,a high-imperceptibility data hiding for JPEG images is proposed based on direction modification.First,this proposed scheme sorts all of the quantized discrete cosine transform(DCT)block in ascending order according to the number of non-consecutive-zero alternating current(AC)coefficients.Then it selects non-consecutive-zero AC coefficients with absolute values less than or equal to 1 at the same frequency position in two adjacent blocks for pairing.Finally,the 2-bit secret data can be embedded into a coefficient-pair by using the filled reference matrix and the designed direction modification rules.The experiment was conducted on 5 standard test images and 1000 images of BOSSbase dataset,respectively.The experimental results showed that the visual quality of the proposed scheme was improved by 1∼4 dB compared with the comparison schemes,and the file size increment was reduced at most to 15%of the comparison schemes.
