Copy Move and Splicing Image Forgery Detection using CNN

Mallick, Devjani; Shaikh, Mantasha; Gulhane, Anuja; Maktum, Tabassum

doi:10.1051/itmconf/20224403052

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…This technique and the CASIAV2 dataset were applied to these models and adapted for binary classification (authentic or forged). Based on the results from these experiments, we can say that using some pre-trained models (VGG16, Xception, ResNet101, and MobileNetV2) could help achieve higher detection accuracy rates compared to the [18,24,25,29,31,32] state-of-the-art. Furthermore, after comparing the resulting charts, graphs, and evaluation metrics for all eight models, it was found that the custom model achieved the secondhighest accuracy score (99.08%) with less than average training parameters compared to the other six pre-trained models used.…”

Section: Discussionmentioning

confidence: 99%

“…VGG19 performed better than DenseNet121 and ResNet152, achieving the lowest accuracy. Our custom model achieved the second highest accuracy after VGG16 compared to the other six pre-trained models and to the state-of-the-art [18], which used Mask R-CNN with MobileNetv1, [24] that used DS-Net(super-BPD) with DCNN, [25] that used SmallerVGGNet, MobileNetV2, [29] that used MTL RestNet18, [31] that used Inceptionv3, and [32] that used ELA, VGG16, VGG19. It is known that models with fewer parameters tend to train faster with fewer computational resources and are less likely to overfit.…”

Section: Model Training and Testing Evaluationmentioning

confidence: 96%

See 1 more Smart Citation

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

NFOR,

Armand,

Kim

2024

Preprint

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In the face of the escalating use of image editing tools and the rapid progress of AI technology, the demand for robust image forgery detection techniques has become more pressing. This study introduces a comprehensive approach to image forgery detection, which integrates image metadata analysis and Error Level Analysis (ELA) with deep learning, specifically Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Using transfer learning, we evaluated seven pre-trained models alongside a custom innovative CNN model using the CASIAV2 dataset for binary classification of authentic and forged images. Metadata was extracted from all the images, and unique feature images were generated and used as inputs for all models by exploiting the differences between compressed and original images through ELA. Among the pre-trained models, VGG16, Xception, ResNet101, and MobileNetV2 demonstrated superior detection accuracy compared to the state-of-the-art models. However, our custom model surpassed six others, achieving an accuracy score of 99.08% with efficient training parameters. This study's results indicate significant advancements in image forgery detection, which has practical applications in forensic tools, automated detection systems, and educational purposes. An enhanced accuracy can benefit industries reliant on image authenticity, improve digital security protocols, protect intellectual property, and influence legal standards. Additionally, the methodology could extend to other domains like document and video authentication.

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Section: Discussionmentioning

confidence: 99%

Section: Model Training and Testing Evaluationmentioning

confidence: 96%

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

NFOR,

Armand,

Kim

2024

Preprint

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“…Several researchers have studied detecting copymove and image splicing using convolutional neural network algorithms. In the paper by Mallick et al [8]. CNN is employed with various models such as ELA, VGG16, and VGG19 to detect copy-move and splicing.…”

Section: Related Workmentioning

confidence: 99%

Image Splicing Forgery Detection using Error Level Analysis and CNN

Irmawati

2023

ULTIMA InfoSys

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The issue of image forgery through splicing has become increasingly relevant in the current digital era. Splicing involves the manipulation of images by combining parts of two or more different images to create a deceptive composite image. This technique can be employed for various purposes, including the dissemination of false information, damaging someone's reputation, or even creating confusion in specific contexts. Several techniques used to detect splicing involve statistical analysis, color analysis, and texture analysis. Additionally, artificial intelligence developments, such as deep learning, have been applied to enhance detection capabilities. In this study, we employed a Convolutional Neural Network (CNN) model to identify image deviations caused by splicing. Optimization was performed on the convolutional layers of the model to improve CNN performance. The integration of Error Level Analysis (ELA) was also implemented to aid in identifying splicing forgeries, where portions of one image are combined with parts of another. Areas that have undergone splicing may exhibit noticeable differences in error levels. The dataset utilized for this research was sourced from DVMM and CUISDE. The validation accuracy results for our CNN model before incorporating ELA were 61% for DVMM and 74% for CUISDE. After adding ELA, the CNN model demonstrated improved detection accuracy, achieving validation rates of 72% for DVMM and 71% for CUISDE.

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“…The accuracy they obtained was 94% when using the YOLO technique. This specific technique was used in many studies in the literature such as [14][15] [16]. These works involve the YOLO framework with deep learning techniques for detecting forged images.…”

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection Using Texture Features of Hidden Forged Regions

Saeed¹,

Hamid²,

Hassan³

2023

Technium

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The recent revolution in technology has not only eased our daily activities at work and home but also introduced new threats. In their daily activities, people exchange a lot of files such as text files, images, videos, etc. that can be used for a variety of purposes. One of the most common types of files is images. These kinds of files can be used to socialize people or spread knowledge among communities. Some of the exchanged images are fake or forged which can lead to the spread of misinformation, which is dangerous. This paper tries to suggest a method for image forgery detection that is copy-move-based. This means a part of the image is used to hide or change other parts in the same image. The suggested method divides an image into several blocks. The feature vectors of the blocks are extracted using a modified Gabor filter. The extracted features are, then, reduced using the principal component analysis technique. The next step is to match the blocks and extract similar ones (duplicated blocks). The findings show that the suggested method is efficient compared to other methods in the literature in terms of detection rate and false positive detection. Also, the proposed method detected forged regions of images when having a 60% of compression rate.

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Copy Move and Splicing Image Forgery Detection using CNN

Cited by 16 publications

References 12 publications

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

Image Splicing Forgery Detection using Error Level Analysis and CNN

Copy-Move Forgery Detection Using Texture Features of Hidden Forged Regions

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