Enhanced Image Tampering Detection using Error Level Analysis and CNN
Received: 12 November 2024 | Revised: 1 December 2024 | Accepted: 4 December 2024 | Online: 11 December 2024
Corresponding author: Ramesh Gorle
Abstract
This paper introduces a novel approach to image tampering detection by integrating Error Level Analysis (ELA) with a Convolutional Neural Network (CNN). Traditional forensic methods, such as ELA and Residual Pixel Analysis (RPA), often struggle to detect subtle or advanced manipulations in digital images. To address these limitations, this method leverages ELA to highlight compression-induced variations and CNN to extract and classify spatial features indicative of tampering. The dataset, consisting of both authentic and tampered images, was preprocessed to generate ELA representations, which were then used to train a CNN model designed to distinguish between authentic and manipulated regions. Extensive experimentation was performed on the CASIA v2.0 dataset, demonstrating significant improvements in detection accuracy, precision, and recall. The proposed framework achieved a detection accuracy of 96.21%, outperforming established deep learning models such as VGG16, VGG19, and ResNet101. These results underscore the potential of combining ELA and CNN in advancing image forensics, offering a robust solution to ensure the integrity of digital content in an era of sophisticated digital manipulation.
Keywords:
error level analysis, convolution neural networks, image forensics, deep learning, digital image integrityDownloads
References
G. Li, X. Yang, and W. Ma, "A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition." arXiv, Feb 22, 2024, https://doi.org/10.48550/arXiv.2402.13545.
R. Gorle and A. Guttavelli, "A novel dynamic image watermarking technique with features inspired by quantum computing principles," AIP Advances, vol. 14, no. 4, Apr. 2024, Art. no. 045024.
J. Xing, X. Tian, and Y. Han, "A Dual-channel Augmented Attentive Dense-convolutional Network for power image splicing tamper detection," Neural Computing and Applications, vol. 36, no. 15, pp. 8301–8316, May 2024.
S. Chopparapu and J. B. Seventline, "An Efficient Multi-modal Facial Gesture-based Ensemble Classification and Reaction to Sound Framework for Large Video Sequences," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11263–11270, Aug. 2023.
X. Yang and J. Zhou, "Research about the Ability of LLM in the Tamper-Detection Area." arXiv, Jan. 24, 2024.
S. Gupta and N. Mohan, "Color Channel Characteristics (CCC) for Efficient Digital Image Forensics," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2555–2561, Feb. 2018.
C. SaiTeja and J. B. Seventline, "A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier," AIP Advances, vol. 13, no. 2, Feb. 2023, Art. no. 025316.
V. Sisaudia and V. P. Vishwakarma, "Approximate regeneration of image using fragile watermarking for tamper detection and recovery in real time," Multimedia Tools and Applications, vol. 83, no. 25, pp. 66299–66318, Jul. 2024.
M. Begum and M. S. Uddin, "Digital Image Watermarking Techniques: A Review," Information, vol. 11, no. 2, Feb. 2020, Art. no. 110.
S. Chopparapu, G. Chopparapu, and D. Vasagiri, "Enhancing Visual Perception in Real-Time: A Deep Reinforcement Learning Approach to Image Quality Improvement," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14725–14731, Jun. 2024.
A. Alzahrani, "Digital Image Forensics: An Improved DenseNet Architecture for Forged Image Detection," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13671–13680, Apr. 2024.
R. M. Bora and M. R. Sanghavi, "Feature Extraction and Analysis of SIFT features for ELA of Authentic and Forged Images," International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 16s, pp. 368–373, Feb. 2024.
S. Fu, Q. Chen, Z. LI, G. Wang, and Y. Lu, "Two-channel progressive feature filtering network for tampered image detection and localization," Journal of Computer Applications, vol. 44, no. 4, 2024, Art. no. 1303.
D. J. Kotti, D. E. Gouthami, D. K. Swapna, and S. Vesalapu, "Morphed Image Detection Using ELA and CNN Techniques," Journal of Pharmaceutical Negative Results, pp. 4069–4078, Dec. 2022.
I. B. K. Sudiatmika, F. Rahman, T. Trisno, and S. Suyoto, "Image forgery detection using error level analysis and deep learning," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 653–659, Apr. 2019.
K. M. Hosny, A. M. Mortda, N. A. Lashin, and M. M. Fouda, "A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network," Applied Sciences, vol. 13, no. 3, Jan. 2023, Art. no. 1272.
S. B. N, G. T. M. Kumar, M. H. C, A. M, and N. Sneha, "Image Forgery Detection Using Ensemble of VGG-16 and CNN Architecture," International Journal of Computational Learning & Intelligence, vol. 2, no. 3, pp. 122–135, Aug. 2023.
D. Goel, "CASIA 2.0 Image Tampering Detection Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/divg07/casia-20-image-tampering-detection-dataset.
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