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 CNN 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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