A Trail Detection Using Convolutional Neural Network

Kim, Jeonghyeok; Lee, Heezin; Kang, Sanggil

doi:10.1007/978-981-10-6520-0_30

Cited by 4 publications

(12 citation statements)

References 3 publications

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“…The approach of adding an additional filter to the CNN to suppress the image content was also recognised by Kim and Lee [ 15 ]. They proposed a model composed of 1 high pass filter, 2 convolutional layers, 2 max pooling layers and 2 fully connected layers.…”

Section: Cnn Architecturementioning

confidence: 99%

“…The high pass filter passes on signals with a frequency higher than a certain cutoff value and attenuates signals with a frequency lower than the cutoff value [ 16 ]. The purpose of this High Class Filter (HPF) in the convolutional network is to extract hidden features within the image [ 15 ]. The full architecture of the CNN model can be seen in Fig.…”

Section: Cnn Architecturementioning

confidence: 99%

“…

Fig. 3 CNN architecture as proposed by Kim and Lee [ 15 ] consisting of 1 HPF, 2 convolutional layers, 2 max pooling layers, and 2 fully connected layers with softmax function for classification. The networks input dimension is a 256 × 256 sized grayscale image.…”

Section: Cnn Architecturementioning

confidence: 99%

“…The results of different numbers of training epochs showed that accuracy does not always increase as learning progresses. For some manipulation operations detection became more accurate, but others decreased in accuracy [ 15 ].…”

Section: Cnn Architecturementioning

confidence: 99%

See 3 more Smart Citations

Interpol review of imaging and video 2016–2019

Geradts

Filius

Ruifrok

2020

Forensic Science International: Synergy

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Section: Cnn Architecturementioning

confidence: 99%

Section: Cnn Architecturementioning

confidence: 99%

“…

Section: Cnn Architecturementioning

confidence: 99%

Section: Cnn Architecturementioning

confidence: 99%

See 2 more Smart Citations

Interpol review of imaging and video 2016–2019

Geradts

Filius

Ruifrok

2020

Forensic Science International: Synergy

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show abstract

“…According to [15] Kim's and Lee's, digital forensics techniques are needed to detect manipulation and fake images used for illegal purposes. Thus, the researchers in this study have been working on an algorithm to detect fake images through deep learning technology, which has achieved remarkable results in modern research.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Fake Images on Social Media using Machine Learning

AlShariah¹,

Saudagar²

2019

IJACSA

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In this technological era, social media has a major role in people's daily life. Most people share text, images, and videos on social media frequently (e.g. Twitter, Snapchat, Facebook, and Instagram). Images are one of the most common types of media share among users on social media. So, there is a need for monitoring of images contained in social media. It has become easy for individuals and small groups to fabricate these images and disseminate them widely in a very short time, which threatens the credibility of the news and public confidence in the means of social communication. This research attempted to propose an approach to extracting image content, classify it and verify the authenticity of digital images and uncover manipulation. Instagram is one of the most important websites and mobile image sharing applications on social media. This allows users to take photos, add digital photographic filters and upload pictures. There are many unwanted contents in Instagram's posts such as threats and forged images, which may cause problems to society and national security. This research aims to build a model that can be used to classify Instagram content (images) to detect any threats and forged images. The model was built using deep algorithms learning which is Convolutional Neural Network (CNN), Alexnet network and transfer learning using Alexnet. The results showed that the proposed Alexnet network offers more accurate detection of fake images compared to the other techniques with 97%. The results of this research will be helpful in monitoring and tracking in the shared images in social media for unusual content and forged images detection and to protect social media from electronic attacks and threats.

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IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

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Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image-based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego-image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego-image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state-of-the-art schemes.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Trail Detection Using Convolutional Neural Network

Cited by 4 publications

References 3 publications

Interpol review of imaging and video 2016–2019

Interpol review of imaging and video 2016–2019

Detecting Fake Images on Social Media using Machine Learning

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

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