A blind recovery technique with integer wavelet transforms in image watermarking

The development of internet technology has simplified the sharing and modification of digital image information. The aim of this study is to propose a new blind recovery technique based on integer wavelets transform (BRIWT) by utilizing their image content. The LSB adjustment technique on the integer wavelet transform is used to embed recovery data into the two least significant bits (LSB) of the image content. Authentication bits are embedded into the current locations of the LSB of the image content, while the recovery information is embedded into different block locations based on the proposed block mapping. The embedded recovery data is securely placed at random locations within the two LSBs using a secret key. A three-layer embedding of authentication bits is used to validate the integrity of the image contents, achieving high precision and accuracy. Tamper localization accuracy is employed to identify recovery bits from the image content. This research also investigates the image inpainting method to enhance recovery from tampered images. The proposed image inpainting is performed by identifying non-tampered pixels in the surrounding tamper localization. The results demonstrate that the proposed scheme can produce highly watermarked images with imperceptibility, with an average SSIM value of 0.9978 and a PSNR value of 46.20 dB. The proposed scheme significantly improves the accuracy of tamper localization, with a precision of 0.9943 and an accuracy of 0.9971. The proposed recovery technique using integer wavelet transforms achieves high-quality blind recovery with an SSIM value of 0.9934 under a tampering rate of 10%. The findings of this study reveal that the proposed scheme improves the quality of blind recovery by 14.2 % under a tampering rate of 80 %

Three layer authentications with a spiral block mapping to prove authenticity in medical images

Digital medical image has a potential to be manipulated by unauthorized persons due to advanced communication technology. Verifying integrity and authenticity have become important issues on the medical image. This paper proposed a self-embedding watermark using a spiral block mapping for tamper detection and restoration. The block-based coding with the size of 3x3 was applied to perform selfembedding watermark with two authentication bits and seven recovery bits. The authentication bits are obtained from a set of condition between sub-block and block image, and the parity bits of each sub-block. The authentication bits and the recovery bits are embedded in the least significant bits using the proposed spiral block mapping. The recovery bits are embedded into different sub-blocks based on a spiral block mapping. The watermarked images were tested under various tampered images such as blurred image, unsharp-masking, copy-move, mosaic, noise, removal, and sharpening. The experimental results show that the scheme achieved a PSNR value of about 51.29 dB and a SSIM value of about 0.994 on the watermarked image. The scheme showed tamper localization with accuracy of 93.8%. In addition, the proposed scheme does not require external information to perform recovery bits. The proposed scheme was able to recover the tampered image with a PSNR value of 40.45 dB and a SSIM value of 0.994

High Capacity Analog Channels for Smart Documents

Widely-used valuable hardcopy documents such as passports, visas, driving licenses, educational certificates, entrance-passes for entertainment events etc. are conventionally protected against counterfeiting and data tampering attacks by applying analog security technologies (e.g. KINEGRAMS®, holograms, micro-printing, UV/IR inks etc.). How-ever, easy access to high quality, low price modern desktop publishing technology has left most of these technologies ineffective, giving rise to high quality false documents. The higher price and restricted usage are other drawbacks of the analog document pro-tection techniques. Digital watermarking and high capacity storage media such as IC-chips, optical data stripes etc. are the modern technologies being used in new machine-readable identity verification documents to ensure contents integrity; however, these technologies are either expensive or do not satisfy the application needs and demand to look for more efficient document protection technologies. In this research three different high capacity analog channels: high density data stripe (HD-DataStripe), data hiding in printed halftone images (watermarking), and super-posed constant background grayscale image (CBGI) are investigated for hidden com-munication along with their applications in smart documents. On way to develop high capacity analog channels, noise encountered from printing and scanning (PS) process is investigated with the objective to recover the digital information encoded at nearly maximum channel utilization. By utilizing noise behaviour, countermeasures against the noise are taken accordingly in data recovery process. HD-DataStripe is a printed binary image similar to the conventional 2-D barcodes (e.g. PDF417), but it offers much higher data storage capacity and is intended for machine-readable identity verification documents. The capacity offered by the HD-DataStripe is sufficient to store high quality biometric characteristics rather than extracted templates, in addition to the conventional bearer related data contained in a smart ID-card. It also eliminates the need for central database system (except for backup record) and other ex-pensive storage media, currently being used. While developing novel data-reading tech-nique for HD-DataStripe, to count for the unavoidable geometrical distortions, registra-tion marks pattern is chosen in such a way so that it results in accurate sampling points (a necessary condition for reliable data recovery at higher data encoding-rate). For more sophisticated distortions caused by the physical dot gain effects (intersymbol interfer-ence), the countermeasures such as application of sampling theorem, adaptive binariza-tion and post-data processing, each one of these providing only a necessary condition for reliable data recovery, are given. Finally, combining the various filters correspond-ing to these countermeasures, a novel Data-Reading technique for HD-DataStripe is given. The novel data-reading technique results in superior performance than the exist-ing techniques, intended for data recovery from printed media. In another scenario a small-size HD-DataStripe with maximum entropy is used as a copy detection pattern by utilizing information loss encountered at nearly maximum channel capacity. While considering the application of HD-DataStripe in hardcopy documents (contracts, official letters etc.), unlike existing work [Zha04], it allows one-to-one contents matching and does not depend on hash functions and OCR technology, constraints mainly imposed by the low data storage capacity offered by the existing analog media. For printed halftone images carrying hidden information higher capacity is mainly attributed to data-reading technique for HD-DataStripe that allows data recovery at higher printing resolution, a key requirement for a high quality watermarking technique in spatial domain. Digital halftoning and data encoding techniques are the other factors that contribute to data hiding technique given in this research. While considering security aspects, the new technique allows contents integrity and authenticity verification in the present scenario in which certain amount of errors are unavoidable, restricting the usage of existing techniques given for digital contents. Finally, a superposed constant background grayscale image, obtained by the repeated application of a specially designed small binary pattern, is used as channel for hidden communication and it allows up to 33 pages of A-4 size foreground text to be encoded in one CBGI. The higher capacity is contributed from data encoding symbols and data reading technique

A dual adaptive watermarking scheme in contourlet domain for DICOM images

<p>Abstract</p> <p>Background</p> <p>Nowadays, medical imaging equipments produce digital form of medical images. In a modern health care environment, new systems such as PACS (picture archiving and communication systems), use the digital form of medical image too. The digital form of medical images has lots of advantages over its analog form such as ease in storage and transmission. Medical images in digital form must be stored in a secured environment to preserve patient privacy. It is also important to detect modifications on the image. These objectives are obtained by watermarking in medical image.</p> <p>Methods</p> <p>In this paper, we present a dual and oblivious (blind) watermarking scheme in the contourlet domain. Because of importance of ROI (region of interest) in interpretation by medical doctors rather than RONI (region of non-interest), we propose an adaptive dual watermarking scheme with different embedding strength in ROI and RONI. We embed watermark bits in singular value vectors of the embedded blocks within lowpass subband in contourlet domain.</p> <p>Results</p> <p>The values of PSNR (peak signal-to-noise ratio) and SSIM (structural similarity measure) index of ROI for proposed DICOM (digital imaging and communications in medicine) images in this paper are respectively larger than 64 and 0.997. These values confirm that our algorithm has good transparency. Because of different embedding strength, BER (bit error rate) values of signature watermark are less than BER values of caption watermark. Our results show that watermarked images in contourlet domain have greater robustness against attacks than wavelet domain. In addition, the qualitative analysis of our method shows it has good invisibility.</p> <p>Conclusions</p> <p>The proposed contourlet-based watermarking algorithm in this paper uses an automatically selection for ROI and embeds the watermark in the singular values of contourlet subbands that makes the algorithm more efficient, and robust against noise attacks than other transform domains. The embedded watermark bits can be extracted without the original image, the proposed method has high PSNR and SSIM, and the watermarked image has high transparency and can still conform to the DICOM format.</p

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

AuSR1 : Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking

With the rapid development of multimedia technology, editing and manipulating digital images have become more accessible than ever. This paper proposed color image authentication based on blind fragile image watermarking for tamper detection and self-recovery named AuSR1. The AuSR1 divides each channel of the cover image into non-overlapping blocks with the size of 2 × 2 pixels. The authentication data is embedded into the original block location, while the recovery data is embedded into the distant location from the original location based on the block mapping algorithm. The watermark data is then embedded into the 2 LSB to achieve high quality of the recovered image under tampering attacks. In addition, the permutation algorithm is applied to ensure the security of the watermark data. The AuSR1 utilizes a three-layer authentication algorithm to achieve a high detection rate. The experimental results show that the scheme produced a PSNR value of 45.57 dB and an SSIM value of 0.9972 of the watermarked images. Furthermore, the AuSR1 detected the tampered area of the images with a high precision value of 0.9943. In addition, the recovered image achieved a PSNR value of 27.64 dB and an SSIM value of 0.9339 on a 50% tampering rate

New watermarking methods for digital images.

The phenomenal spread of the Internet places an enormous demand on content-ownership-validation. In this thesis, four new image-watermarking methods are presented. One method is based on discrete-wavelet-transformation (DWT) only while the rest are based on DWT and singular-value-decomposition (SVD) ensemble. The main target for this thesis is to reach a new blind-watermarking-method. Method IV presents such watermark using QR-codes. The use of QR-codes in watermarking is novel. The choice of such application is based on the fact that QR-Codes have errors self-correction-capability of 5% or higher which satisfies the nature of digital-image-processing. Results show that the proposed-methods introduced minimal distortion to the watermarked images as compared to other methods and are robust against JPEG, resizing and other attacks. Moreover, watermarking-method-II provides a solution to the detection of false watermark in the literature. Finally, method IV presents a new QR-code guided watermarking-approach that can be used as a steganography as well. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b183575

Machine learning based digital image forensics and steganalysis

The security and trustworthiness of digital images have become crucial issues due to the simplicity of malicious processing. Therefore, the research on image steganalysis (determining if a given image has secret information hidden inside) and image forensics (determining the origin and authenticity of a given image and revealing the processing history the image has gone through) has become crucial to the digital society. In this dissertation, the steganalysis and forensics of digital images are treated as pattern classification problems so as to make advanced machine learning (ML) methods applicable. Three topics are covered: (1) architectural design of convolutional neural networks (CNNs) for steganalysis, (2) statistical feature extraction for camera model classification, and (3) real-world tampering detection and localization. For covert communications, steganography is used to embed secret messages into images by altering pixel values slightly. Since advanced steganography alters the pixel values in the image regions that are hard to be detected, the traditional ML-based steganalytic methods heavily relied on sophisticated manual feature design have been pushed to the limit. To overcome this difficulty, in-depth studies are conducted and reported in this dissertation so as to move the success achieved by the CNNs in computer vision to steganalysis. The outcomes achieved and reported in this dissertation are: (1) a proposed CNN architecture incorporating the domain knowledge of steganography and steganalysis, and (2) ensemble methods of the CNNs for steganalysis. The proposed CNN is currently one of the best classifiers against steganography. Camera model classification from images aims at assigning a given image to its source capturing camera model based on the statistics of image pixel values. For this, two types of statistical features are designed to capture the traces left by in-camera image processing algorithms. The first is Markov transition probabilities modeling block-DCT coefficients for JPEG images; the second is based on histograms of local binary patterns obtained in both the spatial and wavelet domains. The designed features serve as the input to train support vector machines, which have the best classification performance at the time the features are proposed. The last part of this dissertation documents the solutions delivered by the author’s team to The First Image Forensics Challenge organized by the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. In the competition, all the fake images involved were doctored by popular image-editing software to simulate the real-world scenario of tampering detection (determine if a given image has been tampered or not) and localization (determine which pixels have been tampered). In Phase-1 of the Challenge, advanced steganalysis features were successfully migrated to tampering detection. In Phase-2 of the Challenge, an efficient copy-move detector equipped with PatchMatch as a fast approximate nearest neighbor searching method were developed to identify duplicated regions within images. With these tools, the author’s team won the runner-up prizes in both the two phases of the Challenge