Preserving Spatial Information to Enhance Performance of Image Forgery Classification

Phan-Xuan, Hanh; Le-Tien, Thuong; Nguyen-Chinh, Thuy; Do-Tieu, Thien; Nguyen-Van, Qui; Nguyen-Thanh, Tuan

doi:10.1109/atc.2019.8924504

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Most splicing detection algorithms [17][18][19] based on CNNs can only deduce whether a given image is tampered with but cannot localize the tampered area. Zhang et al [20] made a preliminary attempt to locate the tampering area, but their method can only detect some rough square areas.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization

2022

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The goal of image splicing localization is to detect the tampered area in an input image. Deep learning models have shown good performance in such a task, but are generally unable to detect the boundaries of the tampered area well. In this paper, we propose a novel deep learning model for image splicing localization that not only considers local image features, but also extracts global information of images by using a multi-scale guided learning strategy. In addition, the model integrates spatial and channel self-attention mechanisms to focus on extracting important features instead of restraining unimportant or noisy features. The proposed model is trained on the CASIA v2.0 dataset, and its performance is tested on the CASIA v1.0, Columbia Uncompressed, and DSO-1 datasets. Experimental results show that, with the help of the multi-scale guided learning strategy and self-attention mechanisms, the proposed model can locate the tampered area more effectively than the state-of-the-art models.

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Section: Deep Learning Methodsmentioning

confidence: 99%

A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization

2022

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“…It fused the streams of the images to localize the manipulated portions of the images. A deep learning model was proposed in [23] where the original image was manipulated in shape and size to detect the forgery. It used the MobileNetV2 model [22] for the detection of image modification.…”

Section: Related Workmentioning

confidence: 99%

“…The motivation to use lightweight models in favour to prevent overfitting of the convolutional neural network (CNN) architectures and can be easily deployed on resource constrained hardware and can learn enriched representations [19][20][21][22][23]. ShuffleNet makes more feature map channels for a given computation complexity budget [24], which helps to encode more information and is especially important to the efficiency of small networks.…”

Section: Introductionmentioning

confidence: 99%

Image forgery detection based on fusion of lightweight deep learning models

Doegar¹,

Hiriyannaiah²,

Matt³

et al. 2021

Turk J Elec Eng & Comp Sci

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Image forgery detection is one of the key challenges in various real time applications, social media and online information platforms. The conventional methods of detection based on the traces of image manipulations are limited to the scope of predefined assumptions like hand-crafted features, size and contrast. In this paper, we propose a fusion based decision approach for image forgery detection. The fusion of decision is based on the lightweight deep learning models namely SqueezeNet, MobileNetV2 and ShuffleNet. The fusion decision system is implemented in two phases. First, the pretrained weights of the lightweight deep learning models are used to evaluate the forgery of the images. Secondly, the fine-tuned weights are used to compare the results of the forgery of the images with the pre-trained models. The experimental results suggest that the fusion based decision approach achieves better accuracy as compared to the state-of-the-art approaches.

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Combining Edge-Guided Attention and Sparse-Connected U-Net for Detection of Image Splicing

Wan,

Su,

Luo

et al. 2023

Lecture Notes in Computer Science

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Preserving Spatial Information to Enhance Performance of Image Forgery Classification

Cited by 9 publications

References 16 publications

A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization

A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization

Image forgery detection based on fusion of lightweight deep learning models

Combining Edge-Guided Attention and Sparse-Connected U-Net for Detection of Image Splicing

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