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State-of-the-art multi-stream methods for image manipulation detection suffer from gaps between features. Moreover, the rapid development of GANs makes it an emerging method of image tampering. However, existing natural scene image tampered datasets are limited to manual tampering. In this paper, we address these two issues. Firstly, we propose a novel two-stream multi-modality image manipulation detection model (MM-net) that abandons the way of fusion. The main idea of the proposed model is to exploit one stream to guide the learning of the other stream through attention mechanism. Therefore, our approach enjoys the benefits of the multi-stream methods while avoiding the semantic gaps caused by bridging gaps between different streams. Secondly, we build the first tampered dataset of natural images based on GANs, pushing manipulation detection toward more realistic and challenging scenarios. Extensive experimental results demonstrate that our model outperforms state-of-the-art approaches on both manually and GANs tampered images.

Deepfake image manipulation has become a serious security threat to the social network. Currently, there are limited studies on protective methods that are against Deepfake image manipulation. To tackle this problem, we here propose an adversary attack based smart watermark model, which adds unperceptive watermarks to images so that the images become adversary examples to Deepfake models. When the Deepfake manipulates these watermarked images, the manipulated images become blur. The manipulation thus can be easily recognized by human and machines. Our experiments have shown that our model outperforms the SOTA and can be used to effectively prevent Deepfake manipulation.

Image data augmentation is a way that makes it possible to increase the diversity of available data without actually collecting new data. In this study, researchers have evaluated the application of image manipulation with the Thatcher effect, double illusion, and inversion on the performance of face detection for data augmentation needs where the data obtained has a weakness that is the limited amount of data to create a training model. The purpose of this research is to increase the diversity of the data so that it can make predictions correctly if given other similar datasets. To perform face detection on images, it is done using YOLOv3 then comparing the accuracy results from the dataset after and before adding data augmentation.

The aim of the present work is to assess the effect of window width and window level selection on feature size in a 2D displayed multimodality images. DICOM images of CT, MR and PET were retrieved and retrospectively analyzed using a local PACS system running an image manipulation software AGFA NX v2.0 that supports several windows for each image modality. The images analyzed were phantom based using standardized NEMA ICE body phantom that consist of six fillable variable spheres sizes. A NOVA t-test was used for statistical difference studies. Using bone and abdomen/ pelvis CT windows resulted in a sphere underestimation of up to 3 mm. While using T1 and T2 MRI windows yielded an underestimation of up to 3.5 mm especially in the small spheres. Significant spheres size differences were seen with PET windows shown an overestimation of up to 7 mm using CE equal and thermal windows. Improper selection of windowing while viewing 2D displayed images can yield considerable inconsistency leading to unreliable measurement.

Coherent rendering in augmented reality deals with synthesizing virtual content that seamlessly blends in with the real content. Unfortunately, capturing or modeling every real aspect in the virtual rendering process is often unfeasible or too expensive. We present a post-processing method that improves the look of rendered overlays in a dental virtual try-on application. We combine the original frame and the default rendered frame in an autoencoder neural network in order to obtain a more natural output, inspired by artistic style transfer research. Specifically, we apply the original frame as style on the rendered frame as content, repeating the process with each new pair of frames. Our method requires only a single forward pass, our shallow architecture ensures fast execution, and our internal feedback loop inherently enforces temporal consistency.

Particleboards may exhibit several defect types caused by a variety of sources during the manufacturing process. It is essential to quickly determine when a defect is present and localize the fault so that the board can either be fixed or discarded. Several methods have been already been developed to address this issue to varying degrees of success. In this work, a novel process is presented which quickly determines whether a defect exists or not using traditional machine learning techniques on a bivariate color histogram of the particleboard and then localize the defect using automated image manipulation techniques. The workflow of quickly determining if a defect is present then using a more computationally intensive technique to localize and classify the defect can be extended to use other methods or even to other processes.

The widespread availability of image editing tools and improvements in image processing techniques allow image manipulation to be very easy. Oftentimes, easy-to-use yet sophisticated image manipulation tools yields distortions/changes imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, including the insertion of objects to hide existing scenes and structures. In this paper, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives., Accepted to the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

Image manipulation techniques are getting common among communities causing the growth of hoax information. Various algorithms began to be developed with the aim of detecting fake images and one of them is detection trace demosaicing techniques. The demosaicing trace detection technique works by reading the changes that occur in the color values within the image area that have been forged. This research aim to create web-based application to detecting fake images by combining web technology and algorithm. In this research, the web application managed to produce an accuracy of 72 percent in detecting fake images.

Image manipulation represents a true “art”, which takes into account the images with a lot of their properties. There are many ways a digital photo can be manipulated, so the forger can use one or more combinations of these in order to make their detection more difficult. On the other hand, only one detection technique is specific to a single image manipulation. For instance, a duplication detection method is based on an existing correlation between the segment of the original image and the copied image. This paper presents a robust Java implementation of an algorithm detecting such a Copy-Move forged pictures.

Median filtering is one of the most popular image anti-forensic technique. Due to its nonlinearity, median filtering effectively hides traces of different types of image manipulation and therefore is integrated into several Anti-Forensic algorithms. As a result, the detection of median filtering has become of great significance for forensic analysis. To this end, several median filtering detectors have been proposed. In order to counter this, various anti-forensic techniques have also been suggested. In this paper we propose an effective anti-forensic technique that performs the median filtering operation on the image while ensuring minimal change in its spatial characteristics. The anti-forensic attack is formulated as an optimization problem and is constrained by a difference of Anisotropic and Isotropic Total Variation regularization. We demonstrate the effectiveness of the proposed technique by performing various experiments on popular median filtering detectors and observing the degradation of their performance on the anti-forensically generated images.

The manipulation of panoramic/wide-angle images is usually achieved via image warping. Though various techniques have been developed for preserving shapes and straight lines for warping, these are not sufficient for panoramic/wide-angle images. The image projections will turn the straight lines into curved “geodesic lines”, and it is fundamentally impossible to keep all these lines straight. In this work, we propose a geodesic-preserving method for content-aware image warping. An energy term is introduced to preserve the geodesic appearance of the geodesic lines, and can be used with shape-preserving terms. Our method is demonstrated in various applications, including rectangling panoramas, resizing panoramic/wide-angle images, and wide-angle image manipulation. An extension to ellipse preservation for general images is also presented.

The CCD slow-motion camera was used for continuously shooting granulometric micro-image in order to reaLize dynamically and continuously gathering granulometric micro-images. The VC.NET in VS2008 IDE was used for developing the analytical software for dynamic granulometer. It can do real-time analyses for particles in consideration of the dynamics and continuity of granulometric micro-image continuous acquisition. After experimental verification, the software can be used for real-time detection of various powders.

Image manipulation has become commonplace with growing easy access to powerful computing abilities. One of the most common types of image forgeries is the copy-paste forgery, wherein a region from an image is replaced with another region from the same image (with possible transformations). A brute-force approach to finding identical regions suffers from many problems, including inability to detect transformed regions. In this paper, we propose a novel technique based on transform-invariant features. These are obtained by adapting the MPEG-7 image signature tools to specifically deal with copy-paste forgeries. Results are provided to justify the application of these tools in copy-paste forgery detection.

This article discusses how to use Babanski's theory of the optimization of teaching process to guide the network instructional design in Blended learning. The instructional design and practice of network course "Photoshop Image Manipulation" has been given as an illustrative example, from the three stages of teaching process, that is preparation, conducting, and analysis results phase, teaching process is regarded as a system and studied synthetically. The instructional design process model for blended learning is proposed to promote the optimization of network teaching process, simultaneously enhance the effectiveness of network teaching. It has been proven to receive good instructional evaluations and help us to find ways to comprehensively improve teaching quality.

The EnforManga is an interactive system for creating comic books based on the simple manipulation of images. To create a comic book that is similar to an actual comic book, the system provides two types of deformations: background and character, which translate images more comically. As a result, the deformed comic book is more like an actual comic book than comics created using conventional systems. Moreover, the manipulations in our system are based on simple drag and drop, so even beginners and children can easily create a comic book.

This paper describes a low-cost distributed computing approach for the post-processing of videos. It has been implemented to support the creation of composite videos from various video sources, and supports image manipulation on individual frames of the video. The principle deployment scenario for this work has been to support the automatic creation of output video based on traditional face-to-face lectures. Experimental results suggest that this approach can lead to significant speedups with a reduction in operating costs by leveraging existing underutilized equipment.

The diagnose instrument of Hess screen is an important instrument for ophthalmic diagnoses. It is mainly used for the measurement of paralytic strabismus. This article is about a digital diagnosis instrument of Hess screen for paralytic strabismus, using image manipulation technology and software technology, based on method of making a traditional Hess screen, medical theory of Hess screen diagnosis and oculomotor muscle constitution. This instrument is composed by diagnosis system of Hess screen for strabismus, LCD screen, computer, and red/blue goggles. The diagnosis system of Hess screen for strabismus is consist of automated Hess screen check up and assistant Hess screen check up, the management system of medical record and the result analytic system. It provides integration function of diagnosis of strabismus, data record and analyze. The instrument uses 20 in LCD screen to display and JAVA to implement the function. The clinical test in some hospitals shows: this instrument is convenience, nicety, quick, no hurt, and provides contrast diagnose medical record and after operation medical record. In the end of the article shows analysis of instrument's error based on the clinical data.

Shape discrimination occupied a very important place in pattern recognition and image manipulation. However, because of the affection of many factors, the geometric varieties of shape will happen. It is needed to find the invariant feature values in all kind of instances. So, this paper proposes some new formulas of moment invariants that are defined united moments. The rotation, translation and scaling can be discretely kept invariant to region, closed and unclosed boundary. And the experiments show the effectivity of the united moments.

This paper presents and analyzes a new method for image manipulation in communication systems. The main goals are to optimize the use of image manipulation resources and this optimization will be accomplished with a high image display quality. Based on results, we conclude that our main goals were achieved.