Digital Photo Forensics

Krawetz, N.

doi:10.1002/9781118798706.hdi044

Cited by 11 publications

(14 citation statements)

References 43 publications

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“…The first and second columns, respectively, show the altered original image and the corresponding ground truth, while the remaining columns show the outcomes of seven different detection methods. The tampered area cannot be precisely located using the conventional detection techniques ELA 34 and CFA, 35 as shown in Fig. 11.…”

Section: Methodsmentioning

confidence: 99%

Dual encoder network with efficient channel attention refinement module for image splicing forgery detection

Tan,

Zhang,

Wang

et al. 2023

J. Electron. Imag.

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.In the realm of picture forensics, it might be difficult to find and locate an image-splicing forgery. To improve the accuracy of the picture forensic evaluation, we introduce a dual encoder network (DAE-Net) with an efficient channel attention (ECA) module. The ECA module creates a fusion approach with an attention mechanism that enables the model to concentrate on local objects’ tampering characteristics and increases the accuracy of multi-region tampering identification. We suggest combining a dual-coding network with a multi-scale dilated convolutional feature fusion module to better detect small target tampering zones. Experimental evidence suggests that DAE-Net outperforms state-of-the-art methods. The attack experiments also demonstrate the DEA-Net model’s stability and noise resistance.

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Section: Methodsmentioning

confidence: 99%

Dual encoder network with efficient channel attention refinement module for image splicing forgery detection

Tan,

Zhang,

Wang

et al. 2023

J. Electron. Imag.

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show abstract

“…ELA is utilised for determining the parts of a picture with varying compression rates. A JPEG picture should have an identical overall image quality [ 32 , 33 , 34 ]. If a portion of the image has a significantly varying error rate, this might suggest a digital change.…”

Section: Methodsmentioning

confidence: 99%

AI bot to detect fake COVID‐19 vaccine certificate

Arif

Shamsudheen

Ajesh

et al. 2022

IET Information Security

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As the world is now fighting against rampant virus COVID-19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non-COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture-based feature extraction for extracting logo, symbol and for the signature we extract Crest-Trough parameter, and e) Classification using Den-seNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1-score, and computation time over state-of-art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201-LBP) outperforms with an accuracy of 0.94.

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“…The proposed model is compared with various traditional and deep learning methods. Traditional methods such as ELA [5], NOI [25], BAG [26], DCT [6], CFA [7], SRM [8], JPEG Compression Model [27], etc. which uses hand-crafted features to detect forgery.…”

Section: Baseline Modelsmentioning

confidence: 99%

“…The second is locating and characterizing different tampering artifacts hidden in a tampered image. Traditional methods use hand‐crafted features and visible artifacts such as error level analysis (ELA) [5], discrete cosine transform (DCT) [6], color filter array (CFA) [7], and steganalysis‐rich model (SRM) [8] to detect hidden artifacts and inconsistencies in Image. The problem with these methods is that they can only apply to a specific type of Image manipulation type.…”

Section: Introductionmentioning

confidence: 99%

ForensicNet: Modern convolutional neural network‐based image forgery detection network

Yadav

2023

Journal of Forensic Sciences

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The advancements in Image editing techniques produce realistic‐looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth‐wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth‐wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state‐of‐the‐art methods by a large margin.

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Digital Photo Forensics

Cited by 11 publications

References 43 publications

Dual encoder network with efficient channel attention refinement module for image splicing forgery detection

Dual encoder network with efficient channel attention refinement module for image splicing forgery detection

AI bot to detect fake COVID‐19 vaccine certificate

ForensicNet: Modern convolutional neural network‐based image forgery detection network

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