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

Krishnaraj, N.; Sivakumar, Bellie; Kuppusamy, Ramya; Teekaraman, Yuvaraja; Thelkar, Amruth Ramesh

doi:10.1155/2022/8501738

Cited by 21 publications

(7 citation statements)

References 22 publications

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“…They utilized the EBSA algorithm to modify the parameters in the Multiclass Support Vector Machine (MSVM) technique to enhance the classification performance. [24] provided an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC), that combined models of generative adversarial networks (GANs) and densely connected networks (DenseNet). The two outputs were merged in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the first layer of an extreme learning machine (ELM) classifier.…”

Section: B Pretrained Network-based Image Forgery Detection Techniquesmentioning

confidence: 99%

Enhancing Digital Image Forgery Detection Using Transfer Learning

Khalil,

Ghalwash,

Elsayed

et al. 2023

IEEE Access

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Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it's crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detecting only one type of forgery, such as image splicing or copy-move that is not applied in real life. This paper proposes an approach, to enhance digital image forgery detection using deep learning techniques via transfer learning to uncover two types of image forgery at the same time, The proposed technique relies on discovering the compressed quality of the forged area, which normally differs from the compressed quality of the rest of the image. A deep learning-based model is proposed to detect forgery in digital images, by calculating the difference between the original image and its compressed version, to produce a featured image as an input to the pre-trained model to train the model after removing its classifier and adding a new fine-tuned classifier. A comparison between eight different pre-trained models adapted for binary classification is done. The experimental results show that applying the technique using the adapted eight different pre-trained models outperforms the state-of-the-art methods after comparing it with the resulting evaluation metrics, charts, and graphs. Moreover, the results show that using the technique with the pre-trained model MobileNetV2 has the highest detection accuracy rate (around 95%) with fewer training parameters, leading to faster training time.

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Section: B Pretrained Network-based Image Forgery Detection Techniquesmentioning

confidence: 99%

Enhancing Digital Image Forgery Detection Using Transfer Learning

Khalil,

Ghalwash,

Elsayed

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…There are some other model which exploits the deep learning based approach such as DLFM-CMDFC [139], deep learning by recompression [140], copy-move image forgery [141], CNN by using the architecture of ResNet50v2 [142].…”

Section: % (Tiff)mentioning

confidence: 99%

A Thorough Investigation on Image Forgery Detection

Kumar¹,

Srivastava²

2023

Computer Modeling in Engineering &Amp; Sciences

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Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

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“…Hernández et al 2018 [20] based on the PV panel and AC supply reference signals. The numerical simulation of this model ensures the accurate operation of the supervisory controller and its algorithm functions in different operating conditions [21][22][23]. The modulation technique created by this type minimizes high-order synchronization while the narrow region of the wide lentil segment reduces low-order synchronization.…”

Section: Proposed Converter Design and Analysismentioning

confidence: 99%

Adaptive Nonlinear Sliding Mode Control for DC Power Distribution in Commercial Buildings

Arasi¹,

Padma²

2023

Intelligent Automation &Amp; Soft Computing

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The developing populace and industrialization power demand prompted the requirement for power generation from elective sources. The desire for this pursuit is solid due to the ever-present common assets of petroleum derivatives and their predominant ecological issues. It is generally acknowledged that sustainable power sources are one of the best answers for the energy emergency. Among these, Photovoltaic (PV) sources have many benefits to bestow a very promising future. If integrated into the existing power distribution infrastructure, the solar source will be more successful, requiring efficient Direct Current (DC)-Alternating Current (AC) conversion. This paper mainly aims to improve controllers' performance between AC/DC Energy sources and the DC loads using the Adaptive Nonlinear Sliding Mode (ANSM) control method. The proposed ANSM method efficiently controls power quality issues, such as transient response, power flow reliability and Total Harmonics Distortion (THD). The proposed controller is applied for both AC/DC and DC/DC converters and the performance of the proposed controller is validated through simulation checking the above parameters. The simulation results confirm ANSM configuration is more reliable and efficient than the existing fuzzy and sliding mode control methods.

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

Cited by 21 publications

References 22 publications

Enhancing Digital Image Forgery Detection Using Transfer Learning

Enhancing Digital Image Forgery Detection Using Transfer Learning

A Thorough Investigation on Image Forgery Detection

Adaptive Nonlinear Sliding Mode Control for DC Power Distribution in Commercial Buildings

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