Data Augmentation of X-ray Images for Automatic Cargo Inspection of Nuclear Items.

As part of establishing a management system to prevent the illegal transfer of nuclear items, automatic nuclear item detection technology is required during customs clearance. However, it is challenging to acquire X-ray images of major nuclear items (e.g., nuclear fuel and gas centrifuges) loaded in cargo with which to train a cargo inspection model. In this work, we propose a new means of data augmentation to alleviate the lack of X-ray training data. The proposed augmentation method generates synthetic X-ray images for the training of semantic segmentation models combining the X-ray images of nuclear items and X-ray cargo background images. To evaluate the effectiveness of the proposed data augmentation technique, we trained representative semantic segmentation models and performed extensive experiments to assess its quantitative and qualitative performance capabilities. Our findings show that multiple item insertions to respond to actual X-ray cargo inspection situations and the resulting occlusion expressions significantly affect the performance of the segmentation models. We believe that this augmentation research will enhance automatic cargo inspections to prevent the illegal transfer of nuclear items at airports and ports.

Lightweight image steganalysis with block-wise pruning.

Image steganalysis is the task of detecting a secret message hidden in an image. Deep steganalysis using end-to-end deep learning has been successful in recent years, but previous studies focused on improving detection performance rather than designing a lightweight model for practical applications. This caused a deep steganalysis model to be heavy and computationally costly, making the model infeasible to deploy in real-world applications. To address this issue, we study an effective model design strategy for lightweight image steganalysis. Considering the domain-specific characteristics of steganalysis, we propose a simple yet effective block removal strategy that progressively removes a sequence of blocks from deep classification networks. This method involves the gradual removal of convolutional neural network blocks, starting from deeper ones. By doing so, the number of parameters and FLOPs are decreased without compromising the detection performance. Experimental results show that our removal strategy makes the EfficientNet-B0 variants 9.58 [Formula: see text] smaller and has 2.16 [Formula: see text] fewer FLOPs than the baseline while retaining detection accuracy of 90.73% and 82.40% that are on par with the baseline on BOSSBase and ALASKA#2 datasets, respectively. Backed by our in-depth analyses, the results indicate that only a few early layers are sufficient for effective image steganalysis.

Exposing Digital Image Forgeries by Detecting Contextual Abnormality Using Convolutional Neural Networks.

Traditionally, digital image forensics mainly focused on the low-level features of an image, such as edges and texture, because these features include traces of the image's modification history. However, previous methods that employed low-level features are highly vulnerable, even to frequently used image processing techniques such as JPEG and resizing, because these techniques add noise to the low-level features. In this paper, we propose a framework that uses deep neural networks to detect image manipulation based on contextual abnormality. The proposed method first detects the class and location of objects using a well-known object detector such as a region-based convolutional neural network (R-CNN) and evaluates the contextual scores according to the combination of objects, the spatial context of objects and the position of objects. Thus, the proposed forensics can detect image forgery based on contextual abnormality as long as the object can be identified even if noise is applied to the image, contrary to methods that employ low-level features, which are vulnerable to noise. Our experiments showed that our method is able to effectively detect contextual abnormality in an image.

Electrodeposition of SnO2 on FTO and its Application in Planar Heterojunction Perovskite Solar Cells as an Electron Transport Layer.

We report the performance of perovskite solar cells (PSCs) with an electron transport layer (ETL) consisting of a SnO2 thin film obtained by electrochemical deposition. The surface morphology and thickness of the electrodeposited SnO2 films were closely related to electrochemical process conditions, i.e., the applied voltage, bath temperature, and deposition time. We investigated the performance of PSCs based on the SnO2 films. Remarkably, the experimental factors that are closely associated with the photovoltaic performance were strongly affected by the SnO2 ETLs. Finally, to enhance the photovoltaic performance, the surfaces of the SnO2 films were modified slightly by TiCl4 hydrolysis. This process improves charge extraction and suppresses charge recombination.