A deep neural network with hybrid spotted hyena optimizer and grasshopper optimization algorithm for copy move forgery detection

Gupta, R.; Singh, Rajesh; Alam, Tanweer; Agarwal, Seema

doi:10.1007/s11042-022-14163-6

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…The existing model has some limitations such as, the deep CMFD using CNN [13] model required huge time to detect forgery from the noisy images. The SSDAE-GOA-SHO [14] model only recognizes the image forgeries with duplicate minimum places and the large block size decreases the computing complexity. The ConvLSTM [15] model maximize the computational complexity and it has existence of various spatial data.…”

Section: Discussionmentioning

confidence: 99%

“…Gupta [14] implemented a deep learning-based technique on Stacked Sparse Denoising Autoencoder (SSDAE) model to detect and classify the images as fake or legitimate. The hidden layers bias and input the weight of the SSDAE model are enhanced by using spotted hyena optimizer (SHO) and grasshopper optimization algorithm (GOA).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This section demonstrates the comparative analysis of MSVM classifier with performance metrics like Accuracy and Precision as shown in Table 5. Existing research such as [13,14,15,16,17] are used for evaluating the ability of this classifier. This proposed method is trained, tested, and validated using MICC-F220, MICC-F600, MICC-F2000 and CASIA 2.0 dataset.…”

Section: Comparative Analysismentioning

confidence: 99%

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Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

2023

IJIES

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Protecting the data against forgery is an important concept and digital images are necessary for exhibiting information. Digital image forgeries are attaching extraordinary patterns to original images and it causes visual heterogeneousness. Image copy-move forgery is a challenging technique, that involves copying part of an image and then pasting the copied part into the same image. In this paper, the golden jackal optimization (GJO) is proposed for feature selection and multi-support vector machine (M-SVM) is proposed for effective classification. The GJO optimizes the feature selection process by iteratively searching for the most informative subset of features. By leveraging the exploration and exploitation of GJO, the algorithm efficiently explores the feature space, selecting relevant features that contribute to accurate forgery classification. This enhance the performance of MSVM by focusing on the most discriminative features. For this work, a dataset named as MICC-F2000 is examined which is managed by 2000 images in this 1300 are original and 700 are forged images. The result shows that the proposed GJO based MSVM model delivers the performance metrics like accuracy, sensitivity, specificity, precision, and MCC values about 99.47 %, 97.01%, 96.51%, 99.62%, and 96.39% respectively which ensures accurate forgery detection compared with existing methods such as SSDAE-GOA-SHO, ConvLSTM, CNN and dual branch CNN methods.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

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Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

2023

IJIES

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“…Ruchi Gupta [16] implemented a DL-based approach on the stacked sparse denoising autoencoder (SSDAE) model to detect and classify the images as fake or legitimate. The hidden layers bias and input weight of the SSDAE approach were enhanced by employing the spotted hyena optimizer (SHO) and grasshopper optimization algorithm (GOA).…”

Section: Literature Surveymentioning

confidence: 99%

“…This section provides the comparative analysis of proposed IBOA-based CNN model with evaluation metrics like precision, accuracy, f1-score, and recall as shown in Table 4. The existing methods such as SSDAE-GOA-SHO [16], DCNN using ResNet-101 [17], and CNN [18] are employed to assess IBOA based CNN performance. The results obtained shows that proposed IBOA-CNN attains better performance compared to the existing methods.…”

Section: Comparative Analysismentioning

confidence: 99%

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network

2024

IJIES

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Copy-move image forgery involves digitally modifying an image by copying and pasting content, hiding foreground objects, or highlighting them through duplication. However, as digital image forgery can become an extremely unsafe and challenging technique to classify images effectively, therefore understanding the detection and classification of real and forgery images is essential. In this research, an improved butterfly optimization algorithmbased convolutional neural network (IBOA-CNN) is proposed for copy-move forgery detection (CMFD), enhancing accuracy and convergence speed by expanding the iteration memory. This proposed approach is used to detect and classify images as original or fake accurately and effectively using deep learning (DL). Initially, the image is obtained by the MICC-F220, MICC-F600, MICC-F2000, and CASIA 2.0 datasets and then image pre-processing is performed by converting Red Green Blue (RGB) into a grayscale image. The Local Binary Pattern (LBP), Wavelet Features (DWT), and ResNet-50 are utilized to extract the features from the images, IBOA is used for feature selection and finally, CNN is employed for the classification to classify CMFD as an original or fake image. Existing methods such as Stacked Sparse Denoising Autoencoder-Spotted Hyena Optimizer-Grasshopper Optimization Algorithm (SSDAE-SHO-GOA), Deep CNN using ResNet-101, and CNN are compared with the IBOA-based CNN approach using MICCF-2000 dataset. The proposed IBOA-based CNN achieves a better accuracy of 97.59%, 99.20%, 99.83%, and 98.92% for MICC-F220, MICC-F600, MICC-F2000, and CASIA 2.0 datasets compared with the existing methods like SSDAE-SHO-GOA, Deep CNN using ResNet-101, and CNN.

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Image forgery detection in forensic science using optimization based deep learning models

Archana,

Biradar,

Dayanand

2023

Multimed Tools Appl

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A deep neural network with hybrid spotted hyena optimizer and grasshopper optimization algorithm for copy move forgery detection

Cited by 8 publications

References 30 publications

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network

Image forgery detection in forensic science using optimization based deep learning models

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