Digital Image Forensics: An Improved DenseNet Architecture for Forged Image Detection


  • Ahmed Alzahrani Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13671-13680 | April 2024 |


Images sent across internet platforms are frequently subject to modifications, including simple alterations, such as compression, scaling, and filtering, which can mask possible changes. These modifications significantly limit the usefulness of digital image forensics analysis methods. As a result, precise classification of authentic and forged images becomes critical. In this study, a system for augmented image forgery detection is provided. Previous research on identifying counterfeit images revealed unexpected outcomes when using conventional feature encoding techniques and machine learning classifiers. Deep neural networks have been also utilized in these efforts, however, the gradient vanishing problem was ignored. A DenseNet model was created to tackle limitations inherent in typical Convolutional Neural Networks (CNNs), such as gradient vanishing and unnecessary layer requirements. The proposed DenseNet model architecture, which is composed of densely connected layers, is designed for precise discrimination between genuine and altered images. A dataset of forged images was implemented to compare the proposed DenseNet model to state-of-the-art deep learning methods, and the results showed that it outperformed them. The recommended enhanced DenseNet model has the ability to detect modified images with an astonishing accuracy of 92.32%.


digital image forensics, deep learning, image forgery detection, convolutional neural networks, DenseNet


Download data is not yet available.


A. Khattak, M. Z. Asghar, M. Ali, and U. Batool, "An efficient deep learning technique for facial emotion recognition," Multimedia Tools and Applications, vol. 81, no. 2, pp. 1649–1683, Jan. 2022.

S.-H. Cho, S. Agarwal, S.-J. Koh, and K.-H. Jung, "Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators," Applied Sciences, vol. 12, no. 14, Jan. 2022, Art. no. 7152.

A. Kuznetsov, "Digital image forgery detection using deep learning approach," Journal of Physics: Conference Series, vol. 1368, no. 3, Aug. 2019, Art. no. 032028.

S. S. Ali, I. I. Ganapathi, N.-S. Vu, S. D. Ali, N. Saxena, and N. Werghi, "Image Forgery Detection Using Deep Learning by Recompressing Images," Electronics, vol. 11, no. 3, Jan. 2022, Art. no. 403.

Y. Wu, W. Abd-Almageed, and P. Natarajan, "BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization," in Computer Vision – ECCV 2018, Cham, 2018, pp. 170–186.

S. I. S. M. Shazuli and A. Saravanan, "Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11555–11560, Oct. 2023.

J. Dong, W. Wang, and T. Tan, "CASIA Image Tampering Detection Evaluation Database," in 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, China, Jul. 2013, pp. 422–426.

K. Team, "Keras documentation: Image data loading," Keras.

"Architecture of DenseNet-121," OpenGenus IQ: Computing Expertise & Legacy, Aug. 26, 2021.

R. Rajkumar, "Deep Learning Feature Extraction Using Attention-Based DenseNet 121 for Copy Move Forgery Detection," International Journal of Image and Graphics, vol. 23, no. 05, Sep. 2023, Art. no. 2350042.

S. Alotaibi, "A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11524–11532, Oct. 2023.

K. H. Hingrajiya and C. Patel, "An Approach for Copy-Move and Image Splicing Forgery Detection using Automated Deep Learning," in 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, Mar. 2023, pp. 1–5.

R. Zhang and J. Ni, "A Dense U-Net with Cross-Layer Intersection for Detection and Localization of Image Forgery," in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, Feb. 2020, pp. 2982–2986.

V. Verma, D. Singh, and N. Khanna, "Block-level double JPEG compression detection for image forgery localization," Multimedia Tools and Applications, vol. 83, no. 4, pp. 9949–9971, Jan. 2024.

C.-C. Hsu, Y.-X. Zhuang, and C.-Y. Lee, "Deep Fake Image Detection Based on Pairwise Learning," Applied Sciences, vol. 10, no. 1, Jan. Art. no. 370, 2020.

D. Alghazzawi, O. Bamasag, A. Albeshri, I. Sana, H. Ullah, and M. Z. Asghar, "Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set," Mathematics, vol. 10, no. 5, Jan. 2022, Art. no. 683.

M. M. H. Milu, M. A. Rahman, M. A. Rashid, A. Kuwana, and H. Kobayashi, "Improvement of Classification Accuracy of Four-Class Voluntary-Imagery fNIRS Signals using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10425–10431, Apr. 2023.

I. Sahib and T. A. A. AlAsady, "Deep learning for image forgery classification based on modified Xception net and dense net," AIP Conference Proceedings, vol. 2547, no. 1, Dec. 2022, Art. no. 060003.


How to Cite

A. Alzahrani, “Digital Image Forensics: An Improved DenseNet Architecture for Forged Image Detection”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13671–13680, Apr. 2024.


Abstract Views: 58
PDF Downloads: 91

Metrics Information