Semantic segmentation of JPEG blocks using a deep CNN for non-aligned JPEG forgery detection and localization

Alipour, Neda; Behrad, Alireza

doi:10.1007/s11042-019-08597-8

Cited by 16 publications

(4 citation statements)

References 42 publications

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“…Depending on the type of forgery, different clues and artifacts can be found in an image to determine whether it is authentic or not. These include, compression noise [54,1], PRNU sensor information [14,13], camera model information [8,41], local noise features [49,16], etc. Earlier forensic methods used algorithms to detect these noise and signal properties using carefully designed filters.…”

Section: Related Workmentioning

confidence: 99%

GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

Das¹,

Islam²,

Amin³

2021

Preprint

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Forensic analysis depends on the identification of hidden traces from manipulated images. Traditional neural networks fail in this task because of their inability in handling feature attenuation and reliance on the dominant spatial features. In this work we propose a novel Gated Context Attention Network (GCA-Net) that utilizes the non-local attention block for global context learning. Additionally, we utilize a gated attention mechanism in conjunction with a dense decoder network to direct the flow of relevant features during the decoding phase, allowing for precise localization. The proposed attention framework allows the network to focus on relevant regions by filtering the coarse features. Furthermore, by utilizing multi-scale feature fusion and efficient learning strategies, GCA-Net can better handle the scale variation of manipulated regions. We show that our method outperforms state-of-the-art networks by an average of 4.2% − 5.4% AUC on multiple benchmark datasets. Lastly, we also conduct extensive ablation experiments to demonstrate the method's robustness for image forensics.

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Section: Related Workmentioning

confidence: 99%

GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

Das¹,

Islam²,

Amin³

2021

Preprint

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“…This is used for the classification as forged or not instead of the fully connected layer. A CNN model is used in [38] that is based on the semantic segmentation of boundaries in the images. The segmentation is carried out based on the semantics of the pixels in the image.…”

Section: Related Workmentioning

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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“…Nevertheless, a large number of forged digital images are generated by malicious use of these techniques, which has led to a serious security and trust crisis of digital multimedia. Therefore, image forensics has gradually attracted an increasing concern in the digital multimedia era, such as JPEG compression forensics [1,2], median filtering detection [3], copy-moving and splicing localization [4,5], universal image manipulation detection [6,7], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics

et al. 2023

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The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. In this paper, a deep network called frequency attention-based dual-stream network (FADS-Net) is proposed for locating the inpainting region. FADS-Net is established by a dual-stream encoder and an attention-based blue-associative decoder. The dual-stream encoder includes two feature extraction streams, the raw input stream (RIS) and the frequency recalibration stream (FRS). RIS directly captures feature maps from the raw input, while FRS performs feature extraction after recalibrating the input via learning in the frequency domain. In addition, a module based on dense connection is designed to ensure efficient extraction and full fusion of dual-stream features. The attention-based associative decoder consists of a main decoder and two branch decoders. The main decoder performs up-sampling and fine-tuning of fused features by using attention mechanisms and skip connections, and ultimately generates the predicted mask for the inpainted image. Then, two branch decoders are utilized to further supervise the training of two feature streams, ensuring that they both work effectively. A joint loss function is designed to supervise the training of the entire network and two feature extraction streams for ensuring optimal forensic performance. Extensive experimental results demonstrate that the proposed FADS-Net achieves superior localization accuracy and robustness on multiple datasets compared to the state-of-the-art inpainting forensics methods.

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Semantic segmentation of JPEG blocks using a deep CNN for non-aligned JPEG forgery detection and localization

Cited by 16 publications

References 42 publications

GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

Image forgery detection based on fusion of lightweight deep learning models

A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics

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