Detecting Non-Aligned Double JPEG Compression Based on Amplitude-Angle Feature

Due to the popularity of JPEG format images in recent years, JPEG images will inevitably involve image editing operation. Thus, some tramped images will leave tracks of Non-aligned double JPEG ( NA-DJPEG ) compression. By detecting the presence of NA-DJPEG compression, one can verify whether a given JPEG image has been tampered with. However, only few methods can identify NA-DJPEG compressed images in the case that the primary quality factor is greater than the secondary quality factor. To address this challenging task, this article proposes a novel feature extraction scheme based optimized pixel difference ( OPD ), which is a new measure for blocking artifacts. Firstly, three color channels (RGB) of a reconstructed image generated by decompressing a given JPEG color image are mapped into spherical coordinates to calculate amplitude and two angles (azimuth and zenith). Then, 16 histograms of OPD along the horizontal and vertical directions are calculated in the amplitude and two angles, respectively. Finally, a set of features formed by arranging the bin values of these histograms is used for binary classification. Experiments demonstrate the effectiveness of the proposed method, and the results show that it significantly outperforms the existing typical methods in the mentioned task.

A Model with Iterative Trials for Correcting Logic Errors in Source Code

It is difficult for students and teachers to detect and correct logic errors in source code. Compilers and integrated development environments (IDEs) have the ability to detect and correct syntax errors but it is also difficult for them to detect and correct logic errors. Although many machine learning approaches have been proposed that can show correction candidates for logic errors, they do not provide guidance concerning how the user should fix them. In this paper, we propose a model for correcting logic errors in a given source code. The proposed model realizes debugging of multiple logic errors in the source code by iterative trials of identifying the errors, correcting the errors, and testing the source code. In this model, in the first stage, a list of correction candidates is provided by a deep learning model, and then the list is given to an editing operation predictor that predicts the editing operation for the correction candidate. To learn the internal parameters of the proposed model, we use a set of solution codes created to solve the corresponding programming tasks in a real e-learning system. To verify the usefulness of the proposed model, we apply it to 32 programming tasks. Experimental results show that the correction accuracy is, on average, 58.64% higher than that of the conventional model without iterative trials.

Maintaining Semantic Information across Generic 3D Model Editing Operations

Many of today’s data models for 3D applications, such as City Geography Markup Language (CityGML) or Industry Foundation Classes (IFC) encode rich semantic information in addition to the traditional geometry and materials representation. However, 3D editing techniques fall short of maintaining the semantic information across edit operations if they are not tailored to a specific data model. While semantic information is often lost during edit operations, geometry, UV mappings, and materials are usually maintained. This article presents a data model synchronization method that preserves semantic information across editing operation relying only on geometry, UV mappings, and materials. This enables easy integration of existing and future 3D editing techniques with rich data models. The method links the original data model to the edited geometry using point set registration, recovering the existing information based on spatial and UV search methods, and automatically labels the newly created geometry. An implementation of a Level of Detail 3 (LoD3) building editor for the Virtual Singapore project, based on interactive push-pull and procedural generation of façades, verified the method with 30 common editing tasks. The implementation synchronized changes in the 3D geometry with a CityGML data model and was applied to more than 100 test buildings.