Digital Image Forgery Detection Using Deep Learning
IEEE BASE PAPER TITLE:
Enhancing Digital Image Forgery Detection Using Transfer Learning
(or)
OUR PROPOSED PROJECT TITLE:
Digital Image Forgery Detection Using Deep Learning
IEEE BASE PAPER ABSTRACT:
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.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
CNN Model Architecture.
OUR PROPSOED PROJECT ABSTRACT:
The advent of digital image manipulation tools has exacerbated the proliferation of image forgeries, necessitating robust solutions for their detection. This project presents a novel approach to address this challenge, utilizing Python and Convolutional Neural Network (CNN) model architecture. The CNN model, employed as the core of our forgery detection system, has exhibited remarkable performance. With a training accuracy of 98% and a validation accuracy of 92%, it showcases its efficacy in distinguishing authentic from tampered images.
The dataset utilized in this study comprises 12,615 images, consisting of 7,492 authentic (real) images and 5,123 tampered (fake) images, providing a diverse and extensive testbed for evaluation. To enhance the precision of our approach, we incorporate Error Level Analysis (ELA) as a preprocessing step. Each image is resized to a standardized 256×256 resolution, after which ELA is applied. ELA aids in the identification of regions within an image that exhibit varying compression levels. In an untampered image, all regions should exhibit uniform compression. Deviations from this uniformity may indicate digital manipulation.
The processed images are stored as numpy arrays for subsequent analysis. Our proposed system leverages the synergy between deep learning through CNNs and the subtleties uncovered by ELA. This combination empowers the model to not only achieve high accuracy but also to provide insights into the specific regions of potential manipulation within an image.
By harnessing the capabilities of Python and a well-structured CNN architecture, this project represents a significant stride towards robust digital image forgery detection, with potential applications in various domains where image authenticity is paramount.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
SOFTWARE REQUIREMENTS:
- Operating system : Windows 10 / 11.
- Coding Language : Python 3.10.9.
- Web Framework : Flask.
- Frontend : HTML, CSS, JavaScript.
REFERENCE:
ASHGAN H. KHALIL, ATEF Z. GHALWASH, HALA ABDEL-GALIL ELSAYED, GOUDA I. SALAMA, AND HAITHAM A. GHALWASH, “Enhancing Digital Image Forgery Detection Using Transfer Learning”, IEEE Access ( Volume: 11), 2023.
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