Image Forgery Detection and Localization Using Block Based and Key-Point Based Feature Matching Forensic Investigation

Monika, .; Bansal, Dipali; Passi, Abhiruchi

doi:10.1007/s11277-022-09898-2

Wireless Pers Commun

2022

DOI: 10.1007/s11277-022-09898-2

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Image Forgery Detection and Localization Using Block Based and Key-Point Based Feature Matching Forensic Investigation

. Monika

Dipali Bansal

Abhiruchi Passi

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Cited by 3 publications

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Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network

Li,

Zhai,

Liu

et al. 2024

ACM Trans. Multimedia Comput. Commun. Appl.

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JPEG image manipulation localization aims to accurately classify and locate tampered regions in JPEG images. Existing image manipulation localization schemes usually consider diverse data streams of spatial domain, e.g. noise inconsistency and local content inconsistency. They, however, easily ignore an objective scenario: data stream features of spatial domain are hard to directly apply to compressed image format, e.g., JPEG, because tampered JPEG images may contain severe re-compression inconsistency and re-compression artifacts, when they are re-compressed to JPEG format. As a result, the traditional localization schemes relying on general data streams of spatial domain may result in a large number of false detection of tampered region in JPEG images. To address the above problem, we a new JPEG image manipulation localization scheme, in which a wide-receptive-field attention network is designed to effectively learn JPEG compressed artifacts. We firstly introduce the wide-receptive-field attention mechanism to re-construct U-Net network, which can effectively capture contextual information of JPEG images and analyze tampering traces from different image regions. Furthermore, a flexible JPEG compressed artifact learning module is designed to capture the image noise caused by JPEG compression, in which the weights can be adjusted flexibly based on image quality, without the need for decompression operations on JPEG images. Our proposed method can significantly strength the differentiation capability of detection model for tampered and non-tampered regions. A series of experiments are performed over different image sets, and the results demonstrate that the proposed scheme can achieve an overall localization performance for multi-scale JPEG manipulation regions and outperform most of state-of-the-art schemes in terms of detection accuracy, generalization and robustness.

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Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network

Li,

Zhai,

Liu

et al. 2024

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

show abstract

Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis

Aguiar,

Nääs,

Okano

2024

Animals

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A critical issue in image analysis for analyzing animal behavior is accurate object detection and tracking in dynamic and complex environments. This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency and segmentation fidelity in object-based image analysis for machine learning applications. The algorithm integrates convolutional operations, quantization strategies, and polynomial transformations to optimize image segmentation in complex visual environments, addressing the limitations of traditional pixel-level and unsupervised methods. This innovative approach enhances object delineation and generates structured metadata, facilitating robust feature extraction and consistent object representation across varied conditions. As empirical validation shows, the proposed preprocessing pipeline reduces computational demands while improving segmentation accuracy, particularly in intricate backgrounds. Key features include adaptive object segmentation, efficient metadata creation, and scalability for real-time applications. The methodology’s application in domains such as Precision Livestock Farming and autonomous systems highlights its potential for high-accuracy visual data processing. Future work will explore dynamic parameter optimization and algorithm adaptability across diverse datasets to further refine its capabilities. This study presents a scalable and efficient framework designed to advance machine learning applications in complex image analysis tasks by incorporating methodologies for image quantization and automated segmentation.

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TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

Zhao

Sun

et al. 2023

Drones

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Drone detection is a significant research topic due to the potential security threats posed by the misuse of drones in both civilian and military domains. However, traditional drone detection methods are challenged by the drastic scale changes and complex ambiguity during drone flight, and it is difficult to detect small target drones quickly and efficiently. We propose an information-enhanced model based on improved YOLOv5 (TGC-YOLOv5) for fast and accurate detection of small target drones in complex environments. The main contributions of this paper are as follows: First, the Transformer encoder module is incorporated into YOLOv5 to augment attention toward the regions of interest. Second, the Global Attention Mechanism (GAM) is embraced to mitigate information diffusion among distinct layers and amplify the global cross-dimensional interaction features. Finally, the Coordinate Attention Mechanism (CA) is incorporated into the bottleneck part of C3, enhancing the extraction capability of local information for small targets. To enhance and verify the robustness and generalization of the model, a small target drone dataset (SUAV-DATA) is constructed in all-weather, multi-scenario, and complex environments. The experimental results show that based on the SUAV-DATA dataset, the AP value of TGC-YOLOv5 reaches 0.848, which is 2.5% higher than the original YOLOv5, and the Recall value of TGC-YOLOv5 reaches 0.823, which is a 3.8% improvement over the original YOLOv5. The robustness of our proposed model is also verified on the Real-World open-source image dataset, achieving the best accuracy in light, fog, stain, and saturation pollution images. The findings and methods of this paper have important significance and value for improving the efficiency and precision of drone detection.

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Image Forgery Detection and Localization Using Block Based and Key-Point Based Feature Matching Forensic Investigation

Cited by 3 publications

References 54 publications

Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network

Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network

Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

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