Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

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

doi:10.3390/info10090286

Cited by 44 publications

(15 citation statements)

References 63 publications

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“…Particularly, based on the developed continuous high-pass filter, they initially determine an effective CNN framework automatically for and adaptively extracting features and propose an RFM model for improving tamper recognition performance and localization solution. Abdalla et al [ 10 ] examine copy-move counterfeit findings with a fusion processing method including an adversarial method and deep convolution method. Four databases were employed.…”

Section: Related Workmentioning

confidence: 99%

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Krishnaraj

Sivakumar

Kuppusamy³

et al. 2022

Computational Intelligence and Neuroscience

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Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model’s weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks’ outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model’s performance. The experimental results established the proposed model’s superiority over recently developed approaches.

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Section: Related Workmentioning

confidence: 99%

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Krishnaraj

Sivakumar

Kuppusamy³

et al. 2022

Computational Intelligence and Neuroscience

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“…These features are combined in a fusion layer to localize the forged region and the source. A data-driven strategy to solve the problem of small datasets was introduced in [16]. This approach is based on two branches.…”

Section: Deep Learningmentioning

confidence: 99%

Localization and Detection of Copy-Move Forgeries in Post-processed Images Using U-Net

2021

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Identification and localization of forgeries in images is of great importance in the field of image processing. Many methods have been introduced to solve this problem. Most provide acceptable results on high resolution images but perform poorly when the resolution is low. Images can be post-processed using techniques such as JPEG compression, brightness change, and the addition of noise to improve the visual quality or create copies for various purposes. Therefore, a new deep learning method is introduced to detect forgeries in images even after post-processing. The proposed model is based on an encoderdecoder architecture designed to learn discriminative features across the boundaries of forged regions. A computer-generated dataset is used for training and evaluation is done using the well-known CoMoFoD dataset. The performance is evaluated using six post-processing techniques, namely brightness change, contrast adjustment, color reduction, image blurring, JPEG compression, and noise addition. To train a robust model for these techniques, data augmentation is employed. The results obtained show that the proposed model outperforms seven recent methods in the literature.

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“…Some research methods have also used GANs [15] for solving image forgery detection. Methods by Younis et al [16], Liu et al [17], Ashraful et al [18], Vladimir et al [19], have made use of GANs in different ways to detect various image manipulations.…”

Section: Related Workmentioning

confidence: 99%

Analysing Statistical methods for Automatic Detection of Image Forgery

Masud¹,

Agarwal²

2021

Preprint

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Image manipulation and forgery detection have been a topic of research for more than a decade now. New-age tools and large-scale social platforms have given space for manipulated media to thrive. These media can be potentially dangerous and thus innumerable methods have been designed and tested to prove their robustness in detecting forgery. However, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only with particular datasets. In this work, we analyze the issue of out-ofdistribution generalisability of the current state-of-the-art image forgery detection techniques through several experiments. Our study focuses on models that utilise handcrafted features for image forgery detection. We show that the developed methods fail to perform well on cross-dataset evaluations and in-the-wild manipulated media. As a consequence, a question is raised about the current evaluation and overestimated performance of the systems under consideration.Note: This work was done during summer research internship at ITMR Lab, IIIT-Allahabad under supervision of Prof. Anupam Agarwal.

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Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Cited by 44 publications

References 63 publications

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Localization and Detection of Copy-Move Forgeries in Post-processed Images Using U-Net

Analysing Statistical methods for Automatic Detection of Image Forgery

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