Digital image forgery detection techniques: a survey

Shwetha, B; Sathyanarayana, S. V.

doi:10.19101/tis.2017.25003

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Anushka Singh, Jyotsna Singh in "Image forgery detection using Deep Neural Network", [3] Due to the availability of deep networks, progress has been made in the field of image recognition. Images and videos are spreading very conveniently and with the avail-ability of strong editing tools the tampering of digital content become easy.…”

Section: Related Workmentioning

confidence: 99%

Image Forgery Detection Based on Parallel Convolutional Neural Networks

T. Blessington,

Ravindra Mule

2024

IJSREM

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Abstract— Due to the availability of deep networks, progress has been made in the field of image recognition. Images and videos are spreading very conveniently and with the availability of strong editing tools the tampering of digital content become easy. To detect such scams, we proposed techniques. In our paper, we proposed two important aspects of employing deep convolutional neural networks to image forgery detection. We first explore and examine different preprocessing method along with convolutional neural networks (CNN) architecture. Later we evaluated the different transfer learning for pre-trained ImageNet(via-fine-tuning) and implement it over our dataset CASIA V2.0. So, it covers the pre-processing techniques with basic CNN model and later see the powerful effect of the transfer learning models. Keywords— image tampering, convolution neural network (CNN), error level analysis (ELA), transfer learning, sharpening filter, fine-tuning

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Section: Related Workmentioning

confidence: 99%

Image Forgery Detection Based on Parallel Convolutional Neural Networks

T. Blessington,

Ravindra Mule

2024

IJSREM

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“…The limitation is that the SIFT algorithm has less false matches. In [67] this review a few specific techniques which includes an Active/ intrusive/non-blind method and a passive/ non-intrusive/ blind method. Passive technology for image forensics is a new research area.…”

Section: IImentioning

confidence: 99%

Forgery Detection on Handwritten Signatures using Convolutional Neural Networks

Pradhan

2023

IJRASET

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Handwritten signatures play a significant part in numerous parts of our day-to-day events and aid in authenticating our personal information all over the world from banks to many government and private institutions. On the other hand, the usage of these handwritten signatures is accompanied by the problems of signature replication, counterfeit signatures and identity theft by both professional and amateur people alike. And so, there is a need for a system to help in differentiating and isolating the real signatures from their copied lookalikes but this task turns out to be really challenging. In recent years, there have been lots of advances in the field of Deep Learning, which is increasingly becoming a part of daily lives. One such deep learning technique called Convolutional Neural Networks, is an interesting tool for analysing pictures and other types of data. This method could be applied to detect and analyse handwritten signatures taken as inputs in order to classify if it is original or forged.

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