A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata

Gani, Gulnawaz; Qadir, Fasel

doi:10.1016/j.jisa.2020.102510

Cited by 48 publications

(21 citation statements)

References 24 publications

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“…Since editing tools advance, the quality of false images rises and it seems to be original images. Furthermore, postprocessing manipulations, such as brightness equalization/changes and JPEG compression, might decrease the traces left by manipulation and make it very complex to identify [ 3 ]. The copy-move forgery detection (CMFD) consists of deep learning- and hand-crafted-based approaches.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Block-based techniques separate the image into overlapping or non-overlapping squares or blocks of circle shapes as shown in Figure 5. Then using an effective feature transform, features can be extracted from each block such as discrete cosine transform [37,38], discrete wavelet transform [39], curvelet transform [40], fourier transform [41,42,43], fast walsh-hadamard transform (fwht), singular value decomposition [44], principal component analysis, intensity, zernike moments [45], and combinations of them. Some of multiscale decomposition transform (MSD) like pyramid and wavelet transform lacks directionality.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Ahmed

Hammad

Jamil

2021

IJEECS

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<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>

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“…Block features are extracted using the normalised moment transform before applying the hash function. Gani et al [12] Cellular Automata are applied to each DCT block feature of the image. The time complexity of the method is very high.…”

Section: Related Workmentioning

confidence: 99%

Keypoint based comprehensive copy‐move forgery detection

Sharma

Roy

Mitra

2020

IET Image Processing

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Verifying the authenticity of a digital image has been challenging problem. The simplest of the image tampering tricks is the copy-move forgery. In copy-move forgery copied portion of the image is pasted on another part of the same image. Geometrical transformations are used on the copied portions of the image before pasting it for the tampered image to look realistic and visually convincing. To make it more complex, other processing approaches may also be applied in the forged region for hiding traces of forgery. These processings are the scale, rotation, JPEG compression, and AWGN. In this paper, an approach based on features of the CenSurE keypoint detector and FREAK descriptor is proposed. This combination has novelty in itself as it has never been used for this purpose before to the best of authors' literature studies. CenSurE detectors are fast and give stable and accurate output even in the case of rotated images, which we club with binary descriptor FREAK. Hierarchical clustering and Neighbourhood search is applied in such a way that it can locate and detect multiple copy-move forgeries. The authors are hopeful that the proposed approach may be used in real-time image authentication and copy-move forgery detection.

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A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata

Cited by 48 publications

References 24 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

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Keypoint based comprehensive copy‐move forgery detection

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