In this paper,two methods are proposed to embed visual watermark into direct binary search(DBS)halftone images,which are called Adjusted Direct Binary Search(ADBS)and Dual Adjusted Direct Binary Search(DADBS).DADBS is...In this paper,two methods are proposed to embed visual watermark into direct binary search(DBS)halftone images,which are called Adjusted Direct Binary Search(ADBS)and Dual Adjusted Direct Binary Search(DADBS).DADBS is an improved version of ADBS.By using the proposed methods,the visual watermark will be embedded into two halftone images separately,thus,the watermark can be revealed when these two halftone images are overlaid.Experimental results show that both methods can achieve excellent image visual quality and decoded visual patterns.展开更多
As one of the most popular digital image manipulations,contrast enhancement(CE)is frequently applied to improve the visual quality of the forged images and conceal traces of forgery,therefore it can provide evidence o...As one of the most popular digital image manipulations,contrast enhancement(CE)is frequently applied to improve the visual quality of the forged images and conceal traces of forgery,therefore it can provide evidence of tampering when verifying the authenticity of digital images.Contrast enhancement forensics techniques have always drawn significant attention for image forensics community,although most approaches have obtained effective detection results,existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format.The detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging task.In this paper,we propose a new CE forensic method based on convolutional neural network(CNN),which is robust to JPEG compression.The proposed network relies on a Xception-based CNN with two preprocessing strategies.Firstly,unlike the conventional CNNs which accepts the original image as its input,we feed the CNN with the gray-level co-occurrence matrix(GLCM)of image which contains CE fingerprints,then the constrained convolutional layer is used to extract high-frequency details in GLCMs under JPEG compression,finally the output of the constrained convolutional layer becomes the input of Xception to extract multiple features for further classification.Experimental results show that the proposed detector achieves the best performance for CE forensics under JPEG post-processing compared with the existing methods.展开更多
Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capac...Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capacity,it inevitably leaves traces of rewriting that can eventually be discovered by the enemy.The method of Steganography by Cover Synthesis(SCS)attempts to construct a natural stego image,so that the cover image is not modified;thus,it can overcome detection by a steganographic analyzer.Due to the difficulty in constructing natural stego images,the development of SCS is limited.In this paper,a novel generative SCS method based on a Generative Adversarial Network(GAN)for image steganography is proposed.In our method,we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network(SSS-GAN)to generate stego images from secret messages.By establishing a mapping relationship between secret messages and semantic category information,category labels can generate pseudo-real images via the generative model.Then,the receiver can recognize the labels via the classifier network to restore the concealed information in communications.We trained the model on the MINIST,CIFAR-10,and CIFAR-100 image datasets.Experiments show the feasibility of this method.The security,capacity,and robustness of the method are analyzed.展开更多
文摘In this paper,two methods are proposed to embed visual watermark into direct binary search(DBS)halftone images,which are called Adjusted Direct Binary Search(ADBS)and Dual Adjusted Direct Binary Search(DADBS).DADBS is an improved version of ADBS.By using the proposed methods,the visual watermark will be embedded into two halftone images separately,thus,the watermark can be revealed when these two halftone images are overlaid.Experimental results show that both methods can achieve excellent image visual quality and decoded visual patterns.
基金This work was supported in part by the National Key Research and Development of China(2018YFC0807306)National NSF of China(U1936212,61672090)Beijing Fund-Municipal Education Commission Joint Project(KZ202010015023).
文摘As one of the most popular digital image manipulations,contrast enhancement(CE)is frequently applied to improve the visual quality of the forged images and conceal traces of forgery,therefore it can provide evidence of tampering when verifying the authenticity of digital images.Contrast enhancement forensics techniques have always drawn significant attention for image forensics community,although most approaches have obtained effective detection results,existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format.The detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging task.In this paper,we propose a new CE forensic method based on convolutional neural network(CNN),which is robust to JPEG compression.The proposed network relies on a Xception-based CNN with two preprocessing strategies.Firstly,unlike the conventional CNNs which accepts the original image as its input,we feed the CNN with the gray-level co-occurrence matrix(GLCM)of image which contains CE fingerprints,then the constrained convolutional layer is used to extract high-frequency details in GLCMs under JPEG compression,finally the output of the constrained convolutional layer becomes the input of Xception to extract multiple features for further classification.Experimental results show that the proposed detector achieves the best performance for CE forensics under JPEG post-processing compared with the existing methods.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.61872384 and61672090).
文摘Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capacity,it inevitably leaves traces of rewriting that can eventually be discovered by the enemy.The method of Steganography by Cover Synthesis(SCS)attempts to construct a natural stego image,so that the cover image is not modified;thus,it can overcome detection by a steganographic analyzer.Due to the difficulty in constructing natural stego images,the development of SCS is limited.In this paper,a novel generative SCS method based on a Generative Adversarial Network(GAN)for image steganography is proposed.In our method,we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network(SSS-GAN)to generate stego images from secret messages.By establishing a mapping relationship between secret messages and semantic category information,category labels can generate pseudo-real images via the generative model.Then,the receiver can recognize the labels via the classifier network to restore the concealed information in communications.We trained the model on the MINIST,CIFAR-10,and CIFAR-100 image datasets.Experiments show the feasibility of this method.The security,capacity,and robustness of the method are analyzed.
