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Detection of double JPEG compression is essential in the field of digital image forensics. Although double JPEG compression detection methods have greatly improved with the development of convolutional neural networks (CNNs), they rely on handcrafted features such as discrete cosine transform (DCT) histograms. In this Letter, the authors propose an end-to-end trainable 3D CNN in the DCT domain for double JPEG compression detection. Moreover, they also propose a new type of module, called feature rescaling , to insert the quantisation table into the network suitably. The experiments show that the proposed method outperforms state-of-the-art methods.

Steganalysis refers to the study of identifying hidden messages in images inserted by steganography. Although detection performance is greatly improved when adopting convolutional neural networks (CNNs), they require sophisticated tricks, such as preprocessing for suppression of image content, using absolute and truncated activation functions, and utilizing domain knowledge. These tricks help networks train stably and mitigate the convergence problem of early stages in training, but they also restrict the flexibility of CNNs, which limits their performance. In this paper, we propose a local-source enhanced residual network (LSER) with end-to-end learning. The LSER is simple in its architecture but has two distinct characteristics from previous methods. First, the LSER uses residual blocks without any normalization. We find batch normalization is an unnecessary module in our framework. Second, a local-source skip connection is added to bypass features of different levels, which allows more abundant feature representation. Moreover, the LSER exhibits state-of-the-art results compared with the existing work in both spatial and JPEG domain steganalysis. Furthermore, a simple self-ensemble method further improves its performance without any side information.

Detecting and localizing image splicing has become essential to fight against malicious forgery. A major challenge to localize spliced areas is to discriminate between authentic and tampered regions with intrinsic properties such as compression artifacts. We propose CAT-Net, an end-to-end fully convolutional neural network including RGB and DCT streams, to learn forensic features of compression artifacts on RGB and DCT domains jointly. Each stream considers multiple resolutions to deal with spliced object’s various shapes and sizes. The DCT stream is pretrained on double JPEG detection to utilize JPEG artifacts. The proposed method outperforms state-of-the-art neural networks for localizing spliced regions in JPEG or non-JPEG images.

Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost function and then adjust the size of an image by removing and duplicating repeatedly calculated seams. Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image forensics. In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving-based image retargeting for reduction and expansion. To attain the ability to learn low-level features, we designed a CNN architecture comprising five types of network blocks specialized for capturing subtle signals. An ensemble module is further adopted to both enhance performance and comprehensively analyze the features in the local areas of the given image. To validate the effectiveness of our work, extensive experiments based on various CNN-based baselines were conducted. Compared to the baselines, our work exhibits state-of-the-art performance in terms of three-class classification (original, seam inserted, and seam removed). In addition, our model with the ensemble module is robust for various unseen cases. The experimental results also demonstrate that our method can be applied to localize both seam-removed and seam-inserted areas.

Double compression (DC) involves compressing a video twice with the video codec. DC usually occurs when a compressed video is manipulated, because most videos are saved in the compression format after manipulating. Given this principle, if we can detect DC traces in videos, we can determine whether a video has been manipulated. Prior works have proposed methods for detecting DC of H.264 videos; however, they have limitations in that they can detect DC only for a limited dataset and set of compression parameters. To overcome these limitations, we propose a two-stream neural network that incorporates two components that analyze intra-coded frames and predictive frames. We explain why the proposed two-stream neural network can detect DC traces in videos more accurately than the prior method, and we demonstrate that the proposed network can detect DC videos that have various contents and parameters through extensive experiments.

Content-aware image resizing is the process of adjusting the size of an image while preserving its important content. Image resizing is used to overcome diversity in resolutions between modules, such as display devices and applications, and can thus be deliberately exploited to distort or remove original content; therefore, detecting such tampering has become an important topic in forensics. This paper proposes a deep neural network architecture to capture subtle local artifacts caused by seam-based image resizing. Unlike past approaches that only classified two classes, our approach is the first attempt to solve a given forensic task with three-class classification: original, seam insertion, and seam carving. The experimental results show that our work performs better than the handcrafted feature-based method and networks designed for different forensic tasks.

The proposed method is a watermarking method for high-quality images that achieves high invisibility and good robustness using a curvelet domain. Recently, there has been a demand for a watermarking technique that does not impair image quality, as interest in high-quality images has increased. To meet this demand, the authors adopted a method of minimising the watermark-embedding energy by using the multi-directional decomposition technique, curvelet transformation. However, if a watermark is inserted into the curvelet domain with the watermarking technique of the conventional domains, it is distorted during the forward and inverse curvelet transform. To solve this problem, they designed a watermarking method by considering the characteristics of the curvelet filter. By minimising the watermark distortion caused by the curvelet transform, high robustness is achieved with small watermarking energy. The proposed method shows very good invisibility and is difficult to distinguish the original. In terms of robustness, the correlation value increased by >80% and BER decreased by >20% compared with previous methods.

As many users of electronic devices have demanded security functions for their devices, many security products have been recently developed and released. Only a few products, unfortunately, are selected by users and most of products are disappeared. The kinds and demand-side market of electronic devices have been dramatically increased. And potential users consider various factors when they purchase security products. However, until now, security products have been developed in no consideration of user’s selection features with effective evident criteria and that’s the reason why most of products are disappeared. Thus this study explores the key factors and preference tendencies that are essentially deliberated when users choose security products. This study provides a decisionmaking model for software developers, users and market strategist. Also this paper proposes the assessment matrix for measuring the factor level to form the criteria which can be utilized in a security product market.

Recently many security products have been released. Only a few products, unfortunately, are selected by users and most of products are disappeard. Potential buyers consider various factors when they purchase security products. However, until now, security products have been developed in no consideration of users‘ selection features with effective evident criteria and that’s the reason why most of products are disappeard. Thus this study explores the key factors and preference tendencies that are essentially deliberated when buyers choose security products. This study provides a decision-making model for software developers, buyers and market strategist. Also this papar proposes the assessment matrix for mesuring the factor levels.

A multichannel lightwave network with a grouping property which exhibits better channel efficiency as the traffic is more likely within groups is proposed. The network capacity and the mean queuing delay in the network are obtained for performance analysis.