Digital image forgery detection using Image hashing

Kekre, H. B.; Mishra, Durgesh Kumar; Halarnkar, Pallavi N.; Shende, Pravin; Gupta, Surbhi

doi:10.1109/icadte.2013.6524736

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…• SURF: It uses a speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC) for the image forgery detection [42].…”

Section: Baseline Models and Metricsmentioning

confidence: 99%

“…The FPR for the baseline 1 [41] is 8%, baseline 2 [42] is 3.64%, baseline 3 [43] is 84%, baseline 4 [44] is 86%, baseline 5 [45] is 2.89%, baseline 6 [46] is 5.45%, baseline 7 [47] is 7.63%, proposed pretrained fusion model is 12.12% and proposed fine-tuned fusion model is 6.06%. The TPR for the baseline 1 [41] is 100%, baseline 2 [42] is 73.64%, baseline 3 [43] is 89%, baseline 4 [44] is 87%, baseline 5 [45] is 96%, baseline 6 [46] is 83.64%, baseline 7 [47] is 97.87%, proposed pretrained fusion model is 100% and proposed fine-tuned fusion model is 100%. Therefore, it can be observed that the fusion model has higher TPR and less FPR as compared to the baseline models due to weight initialization strategy used for the fusion model.…”

Section: Performance Comparison With Fine-tuned Lightweight Modelsmentioning

confidence: 99%

“…FPR (%) TPR (%) SIFT [41] 8 100 SURF [42] 3.64 73.64 DCT [43] 84 89 PCA [44] 86 87 CSLBP [45] 2.89 96 SYMMETRY [46] 5.45 83.64 CLUSTERING strategy [47] 7.63 97.87 Pretrained fusion model (proposed) 12.12 100 Fine-tuned fusion model (proposed) 6.06 100…”

Section: Approachmentioning

confidence: 99%

See 2 more Smart Citations

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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“…• SURF: It uses a speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC) for the image forgery detection [42].…”

Section: Baseline Models and Metricsmentioning

confidence: 99%

Section: Performance Comparison With Fine-tuned Lightweight Modelsmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Zahra Mohamadian and Ali Akbar pouyan proposed a method to detect forgery in digital images in uniform and non-uniform regions in which Zernike moments based detection approaches is used to detect flat copied regions [23]. If the images are rotated, scaled or distorted this method will fail to identify the forged image.…”

Section: Analysis Of Image Splicing Detectionmentioning

confidence: 99%

Analysis of Digital Image Splicing Detection

Burvin¹,

Esther²

2014

IOSRJCE

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The availability of photo manipulation software has made it unprecedentedly easy to manipulate images for malicious purposes. One of the most common forms of digital image or photographic manipulation operation is known as image splicing or image composition. It is a process that's crops and paste regions from same or separate sources. In this paper, we analyze various automatic image forensic techniques for detecting digital image splicing.

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