Enhancing Digital Image Forgery Detection Using Transfer Learning
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.
Transfer learning is a machine learning technique where a model trained on one task is repurposed for another related task. In the context of image forgery detection, you'll use a pre-trained model that has learned features from a large dataset (e.g., ImageNet) and fine-tune it to detect forged images.
Choose a pre-trained convolutional neural network (CNN) model suitable for image classification tasks. Popular choices include VGG, ResNet, Inception, or EfficientNet. These models have learned rich hierarchical representations of visual features that can be useful for forgery detection.
Collect or prepare a dataset of both authentic and forged images. Ensure that the dataset is labeled accurately to distinguish between authentic and forged images. It's crucial to have a diverse and representative dataset to train a robust forgery detection model.
The pre-trained model might be trained on a different dataset than the one used for forgery detection. This domain mismatch can lead to suboptimal performance, as the features learned by the pre-trained model may not be directly applicable to the forgery detection task.
Pre-trained models are often designed for general-purpose tasks like object recognition or scene classification. Adapting these models to the specific nuances of forgery detection, such as subtle manipulations or artifacts, may require extensive fine-tuning and dataset customization.
Fine-tuning a pre-trained model on a relatively small dataset for forgery detection can lead to overfitting, where the model memorizes the training data rather than learning generalizable features. Regularization techniques and careful validation are necessary to mitigate overfitting.
Gather a diverse dataset containing both authentic and forged images. Ensure that the dataset covers various types of forgeries such as copy-move, splicing, and manipulation.
Annotate the dataset accurately to distinguish between authentic and forged images, as well as different types of forgeries if applicable.
Choose a pre-trained convolutional neural network (CNN) model that has shown effectiveness in image classification tasks.
Extract relevant features from the fine-tuned model for forgery detection. These features should capture both low-level and high-level characteristics of authentic and forged images.
Consider using techniques such as feature concatenation or attention mechanisms to combine features from multiple layers of the CNN for improved representation.
Transfer learning allows leveraging pre-trained models that have been trained on large-scale image datasets such as ImageNet. This enables the forgery detection system to benefit from the knowledge and features learned by these models, leading to improved performance without requiring extensive computational resources or labeled data.
Pre-trained models, especially deep convolutional neural networks (CNNs), are adept at extracting hierarchical features from images. By fine-tuning these models on a forgery detection dataset, the system can effectively capture both low-level and high-level features indicative of image manipulations, enhancing its ability to detect various types of forgeries.
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