Electronics

Research

21 pages, 4783 KiB

Open AccessArticle

CECL-Net: Contrastive Learning and Edge-Reconstruction-Driven Complementary Learning Network for Image Forgery Localization

by Gaoyuan Dai, Kai Chen, Linjie Huang, Longru Chen, Dongping An, Zhe Wang and Kai Wang

Electronics 2024, 13(19), 3919; https://doi.org/10.3390/electronics13193919 - 3 Oct 2024

Viewed by 532

Abstract

While most current image forgery localization (IFL) deep learning models focus primarily on the foreground of tampered images, they often neglect the essential complementary background semantic information. This oversight tends to create significant gaps in these models’ ability to thoroughly interpret and understand [...] Read more.

While most current image forgery localization (IFL) deep learning models focus primarily on the foreground of tampered images, they often neglect the essential complementary background semantic information. This oversight tends to create significant gaps in these models’ ability to thoroughly interpret and understand a tampered image, thereby limiting their effectiveness in extracting critical tampering traces. Given the above, this paper presents a novel contrastive learning and edge-reconstruction-driven complementary learning network (CECL-Net) for image forgery localization. CECL-Net enhances the understanding of tampered images by employing a complementary learning strategy that leverages foreground and background features, where a unique edge extractor (EE) generates precise edge artifacts, and edge-guided feature reconstruction (EGFR) utilizes the edge artifacts to reconstruct a fully complementary set of foreground and background features. To carry out the complementary learning process more efficiently, we also introduce a pixel-wise contrastive supervision (PCS) method that attracts consistent regions in features while repelling different regions. Moreover, we propose a dense fusion (DF) strategy that utilizes multi-scale and mutual attention mechanisms to extract more discriminative features and improve the representational power of CECL-Net. Experiments conducted on two benchmark datasets, one Artificial Intelligence (AI)-manipulated dataset and two real challenge datasets, indicate that our CECL-Net outperforms seven state-of-the-art models on three evaluation metrics. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

15 pages, 8441 KiB

Open AccessArticle

PGD-Trap: Proactive Deepfake Defense with Sticky Adversarial Signals and Iterative Latent Variable Refinement

by Zhong Zhuang, Yoichi Tomioka, Jungpil Shin and Yuichi Okuyama

Electronics 2024, 13(17), 3353; https://doi.org/10.3390/electronics13173353 - 23 Aug 2024

Viewed by 738

Abstract

With the development of artificial intelligence (AI), deepfakes, in which the face of one person is changed to another expression of the same person or a different person, have advanced. There is a need for countermeasures against crimes that exploit deepfakes. Methods to [...] Read more.

With the development of artificial intelligence (AI), deepfakes, in which the face of one person is changed to another expression of the same person or a different person, have advanced. There is a need for countermeasures against crimes that exploit deepfakes. Methods to interfere with deepfake generation by adding an invisible weak adversarial signal to an image have been proposed. However, there is a problem: the weak signal can be easily removed by processing the image. In this paper, we propose trap signals that appear in response to a process that weakens adversarial signals. We also propose a new type of adversarial signal injection that allow us to reconstruct and change the original image as far as people do not feel strange by Denoising Diffusion Probabilistic Model (DDPM)-based Iterative Latent Variable Refinement. In our experiments with Star Generative Adversarial Network (StarGAN) trained with the CelebFaces Attributes (CelebA) Dataset, we demonstrate that the proposed approach achieves more robust proactive deepfake defense. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

29 pages, 9748 KiB

Open AccessArticle

Hybrid Machine Learning for Automated Road Safety Inspection of Auckland Harbour Bridge

by Munish Rathee, Boris Bačić and Maryam Doborjeh

Electronics 2024, 13(15), 3030; https://doi.org/10.3390/electronics13153030 - 1 Aug 2024

Viewed by 1326

Abstract

The Auckland Harbour Bridge (AHB) utilises a movable concrete barrier (MCB) to regulate the uneven bidirectional flow of daily traffic. In addition to the risk of human error during regular visual inspections, staff members inspecting the MCB work in diverse weather and light [...] Read more.

The Auckland Harbour Bridge (AHB) utilises a movable concrete barrier (MCB) to regulate the uneven bidirectional flow of daily traffic. In addition to the risk of human error during regular visual inspections, staff members inspecting the MCB work in diverse weather and light conditions, exerting themselves in ergonomically unhealthy inspection postures with the added weight of protection gear to mitigate risks, e.g., flying debris. To augment visual inspections of an MCB using computer vision technology, this study introduces a hybrid deep learning solution that combines kernel manipulation with custom transfer learning strategies. The video data recordings were captured in diverse light and weather conditions (under the safety supervision of industry experts) involving a high-speed (120 fps) camera system attached to an MCB transfer vehicle. Before identifying a safety hazard, e.g., the unsafe position of a pin connecting two 750 kg concrete segments of the MCB, a multi-stage preprocessing of the spatiotemporal region of interest (ROI) involves a rolling window before identifying the video frames containing diagnostic information. This study utilises the ResNet-50 architecture, enhanced with 3D convolutions, within the STENet framework to capture and analyse spatiotemporal data, facilitating real-time surveillance of the Auckland Harbour Bridge (AHB). Considering the sparse nature of safety anomalies, the initial peer-reviewed binary classification results (82.6%) for safe and unsafe (intervention-required) scenarios were improved to 93.6% by incorporating synthetic data, expert feedback, and retraining the model. This adaptation allowed for the optimised detection of false positives and false negatives. In the future, we aim to extend anomaly detection methods to various infrastructure inspections, enhancing urban resilience, transport efficiency and safety. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

19 pages, 4256 KiB

Open AccessArticle

Fine-Grained Few-Shot Image Classification Based on Feature Dual Reconstruction

by Shudong Liu, Wenlong Zhong, Furong Guo, Jia Cong and Boyu Gu

Electronics 2024, 13(14), 2751; https://doi.org/10.3390/electronics13142751 - 13 Jul 2024

Viewed by 750

Abstract

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained [...] Read more.

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained images, which often hamper classification performance. To overcome this, we propose a fine-grained few-shot image classification algorithm based on bidirectional feature reconstruction. This algorithm introduces a Mixed Residual Attention Block (MRA Block), combining channel attention and window-based self-attention to capture local details in images. Additionally, the Dual Reconstruction Feature Fusion (DRFF) module is designed to enhance the model’s adaptability to both inter-class and intra-class variations by integrating features of different scales across layers. Cosine similarity networks are employed for similarity measurement, enabling precise predictions. The experiments demonstrate that the proposed method achieves classification accuracies of 96.99%, 98.53%, and 89.78% on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets, respectively, confirming the method’s efficacy in fine-grained classification tasks. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

13 pages, 7709 KiB

Open AccessArticle

An Enhanced Single-Stage Neural Network for Object Detection in Transmission Line Inspection

by Changyu Cai, Jianglong Nie, Jie Tong, Zhao Chen, Xiangnan Xu and Zhouqiang He

Electronics 2024, 13(11), 2080; https://doi.org/10.3390/electronics13112080 - 27 May 2024

Viewed by 663

Abstract

To address the issue of human object detection in transmission line inspection, an enhanced single-stage neural network is proposed, which is based on the improvement of the YOLOv7-tiny model. Firstly, a lighter GSConv module is utilized to optimize the original ELAN module, reducing [...] Read more.

To address the issue of human object detection in transmission line inspection, an enhanced single-stage neural network is proposed, which is based on the improvement of the YOLOv7-tiny model. Firstly, a lighter GSConv module is utilized to optimize the original ELAN module, reducing the parameters in the network. In order to make the network less sensitive to the targets with an unconventional pose, a module based on CSPNeXt and GSConv is designed and integrated with the ELAN module to extract deep features from the targets. Moreover, a WIoU (Wise Intersection over Union) loss function is utilized to enhance the ability of the YOLOv7-tiny model to detect objects with an unconventional pose and the interference of the background. Finally, the experimental results on human targets in transmission line inspection demonstrate that the proposed network improves detection confidence and reduces missed detection. Compared to the YOLOv7-tiny model, the proposed method promotes the performance of accuracy while reducing the amount of parameters. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

22 pages, 8881 KiB

Open AccessArticle

DCGAN-Based Image Data Augmentation in Rawhide Stick Products’ Defect Detection

by Shuhui Ding, Zhongyuan Guo, Xiaolong Chen, Xueyi Li and Fai Ma

Electronics 2024, 13(11), 2047; https://doi.org/10.3390/electronics13112047 - 24 May 2024

Viewed by 723

Abstract

The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence [...] Read more.

The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence applications. Acquiring a sufficient amount of realistic defect data is challenging, especially during the beginning of product production, due to the occasional nature of defects and the associated costs. Herein, we present a novel image data augmentation method, which is used to generate a sufficient number of defect images. A Deep Convolution Generation Adversarial Network (DCGAN) model based on a Residual Block (ResB) and Hybrid Attention Mechanism (HAM) is proposed to generate massive defect images for the training of deep learning models. Based on a DCGAN, a ResB and a HAM are utilized as the generator and discriminator in a deep learning model. The Wasserstein distance with a gradient penalty is used to calculate the loss function so as to update the model training parameters and improve the quality of the generated image and the stability of the model by extracting deep image features and strengthening the important feature information. The approach is validated by generating enhanced defect image data and conducting a comparison with other methods, such as a DCGAN and WGAN-GP, on a rawhide stick experimental dataset. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

16 pages, 466 KiB

Open AccessArticle

ESFuzzer: An Efficient Way to Fuzz WebAssembly Interpreter

by Jideng Han, Zhaoxin Zhang, Yuejin Du, Wei Wang and Xiuyuan Chen

Electronics 2024, 13(8), 1498; https://doi.org/10.3390/electronics13081498 - 15 Apr 2024

Viewed by 1109

Abstract

WebAssembly code is designed to run in a sandboxed environment, such as a web browser, providing a high level of security and isolation from the underlying operating system and hardware. This enables the execution of untrusted code in a web browser without compromising [...] Read more.

WebAssembly code is designed to run in a sandboxed environment, such as a web browser, providing a high level of security and isolation from the underlying operating system and hardware. This enables the execution of untrusted code in a web browser without compromising the security and integrity of the user’s system. This paper discusses the challenges associated with using fuzzing tools to identify vulnerabilities or bugs in WebAssembly interpreters. Our approach, known as ESFuzzer, introduces an efficient method for fuzzing WebAssembly interpreters using an Equivalent-Statement concept and the Stack Repair Algorithm. The samples generated by our approach successfully passed code validation. In addition, we developed effective mutation strategies to enhance the efficacy of our approach. ESFuzzer has demonstrated its ability to generate code that achieves 100% WebAssembly validation testing and achieves code coverage that is more than twice that of libFuzzer. Furthermore, the 24-h experiment results show that ESFuzzer performs ten times more efficiently than libFuzzer. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

14 pages, 4435 KiB

Open AccessArticle

Feature Reduction Networks: A Convolution Neural Network-Based Approach to Enhance Image Dehazing

by Haoyang Yu, Xiqin Yuan, Ruofei Jiang, Huamin Feng, Jiaxing Liu and Zhongyu Li

Electronics 2023, 12(24), 4984; https://doi.org/10.3390/electronics12244984 - 12 Dec 2023

Cited by 1 | Viewed by 1241

Abstract

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing [...] Read more.

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing CNNs, the output from a dehazing model is often treated as uninformative noise, even though the model’s filters are engineered to extract pertinent features from the images. The standard approach of end-to-end models for dehazing involves noise removal from the hazy image to obtain a clear one. Consequently, the model’s dehazing capacity diminishes, as the noise is progressively filtered out throughout the propagation phase. This leads to the conception of the feature reduction network (FRNet), which is a distinctive CNN architecture that incrementally eliminates informative features, thereby resulting in the output of noise. Our experimental results indicate that the CNN-driven FRNet surpasses previous state-of-the-art (SOTA) methods in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) evaluation metrics. This highlights the effectiveness of the FRNet across various image dehazing datasets. With its reduced overhead, the CNN-based FRNet demonstrates superior performance over current SOTA methods, thereby affirming the efficacy of CNNs in image dehazing tasks. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Image Processing Based on Convolution Neural Network

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI