A Comparative Study for Localization of Forgery Regions in Images
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
As computer technologies and image processing software have advanced, it has become progressively easier to produce simple fake or forged images by altering digital images without leaving any discernible trace. There is a significant need to detect manipulated regions in images in crucial fields such as politics, law, and forensic medicine. In this study, we propose a method that combines the traditional techniques, such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), with the advantages of deep learning methods to detect manipulated regions in forged images. The proposed method involves designing an architecture where DWT and DCT are used in parallel with DenseNet based Convolutional Neural Network (CNN). To evaluate the effectiveness of this method, we implemented three alternative approaches: one that uses only DCT and CNN, another that uses only DWT and CNN, and a third that employs only CNN without either transformation. In total, four different methods were tested on eight datasets, and their performance was compared using metrics such as accuracy, precision, recall, dice similarity coefficient, and F1 score. The results from these comparisons clearly indicate the effectiveness and high classification accuracy of the proposed method. By leveraging the combined strengths of traditional image processing techniques and advanced deep learning algorithms, the proposed method demonstrates superior capability in detecting manipulated regions in forged images, thus offering a robust solution for applications in forensic field.
Açıklama
Anahtar Kelimeler
Forgery, Discrete wavelet transforms, Discrete cosine transforms, Feature extraction, Convolutional neural networks, Accuracy, Transforms, Frequency-domain analysis, Deep learning, Watermarking, Convolutional neural networks (CNN), discrete cosine transform (DCT), discrete wavelet transform (DWT), image forgery detection
Kaynak
Ieee Access
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
13
