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"Digital images Editing."
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How do I do that in Photoshop? : the quickest ways to do the things you want to do, right now!
Offers a guide to Adobe Photoshop intended to allow users to find and use the features they are interested in quickly.
Book
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
Journal Article
Recent advances in digital image manipulation detection techniques: A brief review
•Provides a review of various recent image manipulation detection techniques.•An overview of datasets that are available for image manipulation detection.•Discussion on evaluation parameters used for image manipulation detection.•Comparative tables for various image manipulation detection techniques.•Deep learning-based methods are better than the hand-crafted based methods.
A large number of digital photos are being generated and with the help of advanced image editing software and image altering tools, it is very easy to manipulate a digital image nowadays. These manipulated or tampered images can be used to delude the public, defame a person's personality and business as well, change political views or affect the criminal investigation. The raw image can be mutilated in parts or as a whole image so there is a need for detection of what type of image tampering is performed and then localize the tampered region. Initially, single handcrafted manipulated images were used to detect the only image tampering present in the image but in a real-world scenario, a single image can be mutilated by numerous image manipulation techniques. Nowadays, multiple tampering operations are performed on the image and post-processing is done to erase the traces left behind by the tampering operation, making it more difficult for the detector to detect the tampering. It is seen that the recent techniques that are used to detect image manipulation are based on deep learning methods. In this paper, more focus is on the study of various recent image manipulation detection techniques. We have examined various image forgeries that can be performed on the image and various image manipulation detection and localization methods.
Journal Article
Image forgery techniques: a review
Image forensics is an investigation of digital images to identify manipulations that have been done on them. Nowadays, due to the availability of different low-cost devices for capturing images, digital images are gaining quite a bit of popularity. It occurs frequently that these images are manipulated by mistake or on purpose, resulting in inaccurate information being presented by the image. There is a need to develop techniques to identify forgeries present in digital images used by the media, in court trials, and for maintaining visual records, since digital images are commonly used as evidence through the media, in court, and for maintaining record keeping. A detailed review of various image forgery detection techniques is presented in this article including comparisons between the various methods, pros and cons, and results obtained during the experimentation.
Journal Article
The Art and Technique of Digital Color Correction
How to\" books are a dime a dozen. What makes this book special is that it is also a \"Why\" book. Hullfish sits down with world-class colorists and records not only what they do but why they do it. That's where the magic lies.
\"How\" is the question to ask if you want to become a craftsman. \"Why\" is the question that creates artists. I bought the first edition for \"How\" and came away with a lot of \"Why.\" This edition has lots more of both, with material from several additional world class colorists.
If you want an inside look into the art and craft of the professional colorist there's no better way to do it in book form. Whether you're learning to be a colorist or just want to understand what really happens when you decide something can be \"fixed in post,\" you need to read this book. -Art Adams, cinematographer/educator, ProVideoCoalition.com. This book just keeps getting better with each new edition. Steve Hullfish's approach is designed to teach techniques that transfer to a wide range of popular and accessible color correction tools. The intent is to demystify the process, so readers can learn the concepts and apply them, regardless of whether the software has sliders, wheels or curves. Best of all, Hullfish features extensive tips and tricks from some of the premier colorists in the country, so you can learn from the masters. If you only purchase one book on color correction, this is the essential guide to include in your library. -Oliver Peters, Oliver Peters Peters Post Production Services, LLC A terrific and much-needed book for anybody serious about digital color correction. Starting with the basics, it helps the reader work through a series of specific, well-illustrated examples, covering all the major software applications, and supports the text with insightful comments from prominent working colorists. All in all, it's essential reading for anyone who wants to improve their skills in this rapidly changing field. -Steve Cohen, editor, Emmy and ACE Eddie winner, author of Avid Agility.
eBook
Feature selection for text classification: A review
Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.
Journal Article
How photo editing in social media shapes self-perceived attractiveness and self-esteem via self-objectification and physical appearance comparisons
Background
As photo editing behavior to enhance one?s appearance in photos becomes more and more prevalent on social network sites (SNSs), potential risks are increasingly discussed as well. The purpose of this study is to examine the relationship between photo editing behavior, self-objectification, physical appearance comparisons, self-perceived attractiveness, and self-esteem.
Methods
403 participants completed self-report questionnaires measuring the aformentioned constructs. A parallel-sequential multiple mediation model was conducted to examine the relationship between photo editing behavior and self-esteem considering multiple mediators.
Results
The results indicate that photo editing behavior is negatively related to self-perceived attractiveness and self-esteem mediated via self-objectification and physical appearance comparisons.
Conclusions
The postulated mediation model was justified by our data. Thus, SNS users should be aware of potential negative consequences when using photo editing applications or filters.
Journal Article
Research on the Application of Image Processing and Image Recognition Algorithms in Digital Media in Content Editing and Production
Image processing and image recognition algorithms are essential to content generation and editing in the digital age. They provide creative ways to improve visual quality, streamline processes, and customize user interfaces. In-depth methods for incorporating cutting-edge image processing and image recognition algorithms into digital media content production and editing workflows are covered in this study. Methods: To maximize visual quality, the research first focuses on pre-processing and improving digital images using the median filtering technique. The Histogram of Oriented Gradients (HOG) technique is applied to locate and identify the objects in images, that enabling customized and interactive content modification. After that, the images are segmented using the watershed approach, and the precise classification of the images is achieved by applying the Mayfly Optimized Spatial Graph Recurrent Neural Network (MOSGRNN), which improves content organization and retrieval. Results: The results of the experiments demonstrate that the suggested strategy performs well in all aspects of image processing in terms of accuracy (94.91%), recall (92.70%), recognition speed (44 FPS), and fl-score (93.7%). Conclusion: Content production, editing, and delivery across a variety of platforms might be significantly transformed by research on image processing and image recognition algorithms in digital media.
Journal Article
Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images
Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.
Journal Article
Advanced copy-move forgery detection: utilizing AKAZE in conjunction with SIFT algorithm for image forensics
Digital picture manipulation is becoming common due to the availability of powerful digital technologies and image editing tools. Despite employing editing techniques to improve photo quality, image forgeries pose a notable challenge. Copy-move forgery (CMF) is a common technique used to manipulate images by copying a specific section of a picture and pasting it elsewhere in the same image, thereby undermining the integrity of the digital image. This work presents a highly effective Copy Move Forgery Detection (CMFD) technique that surpasses the performance and accuracy of existing approaches like SURF (Speeded-Up Robust Features) and SIFT (Scale-invariant feature transform). The proposed methodology integrates the SIFT algorithm with AKAZE (Accelerated-Kaze) to identify key points in greyscale pictures. To accurately identify tampered areas, AKAZE and SIFT extract numerous keypoints, excluding smooth regions, with the Histogram of Oriented Gradients (HOG) utilized as the feature descriptor. This methodology enhances both the efficacy and accuracy of forgery detection and localization within smooth areas. This technique enhances the effectiveness and precision of identifying and pinpointing forged areas in smooth zones. The feature vector is precisely categorized in feature space using the sum of squared differences (SSD) and closest neighbor distance ratio (NNDR) to establish an optimal matching. The RANSAC (Random Sample Consensus) algorithm is subsequently applied to eliminate abnormalities found in previous phases. Experimental findings have confirmed the algorithm's superiority over contemporary methodologies based on various metrics, including Precision, Recall, and F- score. Furthermore, the algorithm demonstrates resilience against geometric attacks, such as scaling, rotation, and noise.
Journal Article