Multi-scale attention context-aware network for detection and localization of image splicing

Ren, Ruyong; Niu, Shaozhang; Jin, Junfeng; Zhang, Jiwei; Ren, Hua; Zhao, Xiao‐Fan

doi:10.1007/s10489-022-04421-3

Cited by 11 publications

(6 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A typical AGV system in an intelligent warehouse will be equipped with more than a hundred AGV trolleys operating simultaneously. The trolleys are composed of a power supply system, a control system, and an information system [11], they are also equipped with single or dual motors for basic movements and have steering capabilities. The sorting robot arm grabs the goods and places them on the trolley.…”

Section: Agv Sorting Systemmentioning

confidence: 99%

Cost Efficiency and Profitability through Intelligent Warehouse: Case Study of Unmanned Warehouse “Asia One” in JD Logistics

Chen

2024

SHS Web Conf.

View full text Add to dashboard Cite

With the general environment of consumer purchasing power and the popularity of flexible supply chains, many warehousing companies have been based on their own needs for intelligent warehouse transformation, some warehouses for a comprehensive unmanned transformation, while some through the human-machine collaboration to control storage costs. But most of these warehouses to achieve transformation are only used to store one or a class of goods, such as the medicine and equipment industry. Able to carry out a large number and multiple items stored at the same time, intelligent warehouse construction is not popular enough, this paper will be the existing large-scale intelligent warehouse working principle, efficiency results and profit costs, and other aspects of the analysis to explore the large-scale warehouse intelligent transformation can bring specific benefits to the enterprise. The representatives of large, intelligent warehouses Amazon and Jingdong’s large and unmanned warehouses have a strong benchmark. In this paper, we select Jingdong’s large, unmanned warehouse, “Asia No. 1” and explain the working process and the role of hardware and software systems to find out what advantages the operation of these machines can bring to enterprises, what the competitiveness of intelligent equipment compared with manual labor, and how to help enterprises increase benefits and reduce costs.

show abstract

Section: Agv Sorting Systemmentioning

confidence: 99%

Cost Efficiency and Profitability through Intelligent Warehouse: Case Study of Unmanned Warehouse “Asia One” in JD Logistics

Chen

2024

SHS Web Conf.

View full text Add to dashboard Cite

show abstract

“…However, Bayar et al [18] proposed a constrained convolutional layer (called Bayar layer) that adaptively learns to suppress the image's content and learns manipulation detection features. Several methods [21], [22], [25], [45]- [47] were proposed to leverage both the noise map and content of the image to reduce the risk of losing other useful information in the original RGB view. Zhou et al [25] proposed a two-stream fast R-CNN for image manipulation detection, the RGB image, and its noise counterpart generated by the spatial rich model (SRM) [17].…”

Section: B Localizing Tampered Regionsmentioning

confidence: 99%

IRL-Net: Inpainted Region Localization Network via Spatial Attention

Daryani,

Mirmahdi,

Hassanpour

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Identifying manipulated regions in images is a challenging task due to the existence of very accurate image inpainting techniques leaving almost unnoticeable traces in tampered regions. These image inpainting methods can be used for multiple purposes (e.g., removing objects, reconstructing corrupted areas, eliminating various types of distortion, etc.) makes creating forensic detectors for image manipulation an extremely difficult and time-consuming procedure. The aim of this paper is to localize the tampered regions manipulated by image inpainting methods. To do this, we propose a novel CNNbased deep learning model called IRL-Net which includes three main modules: Enhancement, Encoder, and Decoder modules. To evaluate our method, three image inpainting methods have been used to reconstruct the missed regions in two face and scene image datasets. We perform both qualitative and quantitative evaluations on the generated datasets. Experimental results demonstrate that our method outperforms previous learning-based manipulated region detection methods and generates realistic and semantically plausible images. We also provide the implementation of the proposed approach to support reproducible research via https://github.com/amiretefaghi/IRL-Net

show abstract

“…Many researchers have started to incorporate the attention mechanism in tampering detection. Ren et al [61] proposed a multi-scale attention context-aware network and designed a multi-scale multilevel attention module, which not only effectively solved the inconsistency of features at different scales, but also automatically adjusted the coefficients of features to obtain a finer feature representation. To address the problem of poor accuracy of splicing boundary, Sun et al [62] proposed an edge-enhanced transformer network.…”

Section: Image Splicing Forgery Detectionmentioning

confidence: 99%

Review of Image Forensic Techniques Based on Deep Learning

et al. 2023

View full text Add to dashboard Cite

Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.

show abstract

Multi-scale attention context-aware network for detection and localization of image splicing

Cited by 11 publications

References 52 publications

Cost Efficiency and Profitability through Intelligent Warehouse: Case Study of Unmanned Warehouse “Asia One” in JD Logistics

Cost Efficiency and Profitability through Intelligent Warehouse: Case Study of Unmanned Warehouse “Asia One” in JD Logistics

IRL-Net: Inpainted Region Localization Network via Spatial Attention

Review of Image Forensic Techniques Based on Deep Learning

Contact Info

Product

Resources

About