Convolutional Neural Network for Copy-Move Forgery Detection

Abdalla, Younis E.; Iqbal, M. Tariq; Shehata, Mohamed

doi:10.3390/sym11101280

Cited by 36 publications

(16 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Younis et al in [32] utilized the reliability fusion map for the detection of the forgery. By utilizing the CNNs, Younis et al in [33] classify an image as the original one, or it contains copy-move image forgery. In [34] Vladimir et al, train four models at the same time: a generative annotation model GA, a generative retouching model GR, and two discriminators DA and DR that checks the output of GA and GR.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Forgery Detection Using Deep Learning by Recompressing Images

Ali

Ganapathi

et al. 2022

Electronics

View full text Add to dashboard Cite

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning-based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Image Forgery Detection Using Deep Learning by Recompressing Images

Ali

Ganapathi

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…[10] and identify forged images using SVM or deep neural network. However, in more recently, the popularity and performance deep learning methods has also proven to benefit the forgery detection area [11,12,13,14]. While in some earlier works on deep fakes interpretability also has been considered, this has been restricted for face images.…”

Section: Learning Based Techniquementioning

confidence: 99%

Image Forgery Detection with Interpretability

Katiyar¹,

Bhavsar²

2022

Preprint

View full text Add to dashboard Cite

In this work, we present a learning based method focusing on the convolutional neural network (CNN) architecture to detect these forgeries. We consider the detection of both copy-move forgeries and inpainting based forgeries. For these, we synthesize our own large dataset. In addition to classification, the focus is also on interpretability of the forgery detection. As the CNN classification yields the image-level label, it is important to understand if forged region has indeed contributed to the classification. For this purpose, we demonstrate using the Grad-CAM heatmap, that in various correctly classified examples, that the forged region is indeed the region contributing to the classification. Interestingly, this is also applicable for small forged regions, as is depicted in our results. Such an analysis can also help in establishing the reliability of the classification.

show abstract

“…In 2019, Younis Abdalla, Tariq Iqbal M. and Mohamed Shehata [13] suggested a new scheme built on neural networks and deep learning, focusing on the architectural method of the convolution neural networks (CNN) to improve the identification of copy-move forgeries. A CNN architecture that integrates pre-processing layers to offer satisfactory results is used in the proposed approach.…”

Section: Related Workmentioning

confidence: 99%

Forged Copy-Move Recognition Using Convolutional Neural Network

Sewan

Altaei

2021

ANJS

View full text Add to dashboard Cite

Due to the extreme robust image editing techniques, digital images are subject to multiple manipulations and decreased costs for digital camera and smart phones. Therefore, image credibility is becoming questionable, specifically when images have strong value, such as news report and insurance claims in a crime court. Therefore, image forensic methods test the integrity of the images by applying various highly technical methods set out in the literature. The present work deals with one important research module is the recognition of forged part that applied on copy move forgery images. Two datasets MICC-F2000 and CoMoFoD are used, these datasets are usually adopted in the field of interest. The module concerned with recognizing which is the source image portion and which is the target one of that already detected. Thus, the two detected tampered parts of the image are recognized the original one from them, the other is then referred as forged or tampered part. The proposed module used the buster net of three neural networks that basically adopted the principle of training by using Convolution Neural Network (CNN) to extract the most important features in the images. The first and second networks are parallel working to detect and identify areas that have been tampered with, and then display them through two masks. While the last network classifier takes a copy of these two catchers to decide which is the source image portion from the two detected ones. The achieved recognition results were about F-score 98.98% even if the forged area is rotated or scaled or both of them. Also, the recognition results of the forged image part was 98% when using images do not contributed in the training phase, which refers to that the proposed module is more confident and reliable.

show abstract

Convolutional Neural Network for Copy-Move Forgery Detection

Cited by 36 publications

References 39 publications

Image Forgery Detection Using Deep Learning by Recompressing Images

Image Forgery Detection Using Deep Learning by Recompressing Images

Image Forgery Detection with Interpretability

Forged Copy-Move Recognition Using Convolutional Neural Network

Contact Info

Product

Resources

About