Abstract
To make image information more authentic and complete and to prevent splicing tampered image data from affecting social fairness, this paper proposes an algorithm based on Deep Convolutional Neural Networks to detect splicing tampered digital media image privacy. Building upon deep convolutional feature extraction, this algorithm introduces a self-attention mechanism to enhance focus on tampered regions. For the first time, it innovatively applies a boundary-aware loss function to patch tampering detection, effectively addressing the issue of ambiguous boundary region detection and significantly improving localization accuracy. The experiment was conducted on datasets from the Institute of Automation, Chinese Academy of Sciences, Cover Dataset, National Institute of Standards and Technology datasets, and the 2020 Image Tampering Dataset. The algorithm demonstrated the following performance metrics: area under the receiver operating characteristic curve values of 0.971, 0.961, and 0.987, respectively; accuracy rate of 98.94%; precision rate of 97.12%; recall rate of 99.16%; F1 mean values of 0.941 and 0.952 under gamma ray and noise interference, respectively, indicating strong robustness. These results prove that the proposed algorithm can achieve precise detection of splicing tampered image privacy. It effectively addresses the problem of insufficient detection accuracy in some existing methods. It also promotes the intelligent development of detection and contributes to building a more authentic information environment in society.
References
Jȩdrasiak K, Wolañski R. An image Analysis Algorithm for Detecting Modified Content on the Internet Against Cybercrime. A Technique for Estimating the Probability of Modification. Safety & Fire Technology, 2024, 63(1): 88–94.
Bai X, Bai Y. Equilibrium Strategy of Attack and Defense in Computer Networks Based on Markov Signal Game Theory. Journal of Cyber Security and Mobility, 2025, 14(1): 127–154. DOI:0.13052/jcsm2245-1439.1416.
Hasanvand M, Nooshyar M, Moharamkhani E, Selyari A. Machine Learning Methodology for Identifying Vehicles Using Image Processing. AIA, 2023, 3(1): 170–178.
Somsuk K. Enhanced Algorithm for Recovering RSA Plaintext when Two Modulus Values Share at least One Common Prime Factor. Journal of Cyber Security and Mobility, 2025, 14(2): 433–456. DOI:10.13052/jcsm2245-1439.1427.
Dutta M, Saini J. A systematic literature review on image splicing detection and localization using emerging technologies. Multimedia Tools and Applications, 2025, 84(19): 20721–20756.
Li X, Zhang J. Multi-source Data Fusion for Real-time Cybersecurity Situational Awareness and Visualization. Journal of Cyber Security and Mobility, 2025, 14(4): 955–980. DOI:10.13052/jcsm2245-1439.1448.
Xing J, Tian X, Han Y. A Dual-channel Augmented Attentive Dense-convolutional Network for power image splicing tamper detection. Neural Computing and Applications, 2024, 36(15): 8301–8316.
Kumari R, Garg H. Image splicing forgery detection: A review. Multimedia Tools and Applications, 2025, 84(8): 4163–4201.
Akram A, Jaffar M A, Rashid J, Boulaaras S M, Faheem M. CMV2U-net: au-shaped network with edge-weighted features for detecting and localizing image splicing. Journal of Forensic Sciences, 2025, 70(3): 1026–1043.
Hu J, Xue R, Teng G, et al. Image splicing manipulation location by multi-scale dual-channel supervision. Multimedia Tools and Applications, 2024, 83(11): 31759–31782.
Chen E. Analysis of E-commerce Security Protection Technology Based on YOLO Algorithm Optimized by Lightweight Neural Network. Journal of Cyber Security and Mobility, 2025, 14(4): 849–876. DOI:10.13052/jcsm2245-1439.1444.
Kumari R, Garg H. Image splicing forgery detection: A review. Multimedia Tools and Applications, 2025, 84(8): 4163–4201.
Dutta M, Saini J. A systematic literature review on image splicing detection and localization using emerging technologies. Multimedia Tools and Applications, 2025, 84(19): 20721–20756.
Subha S, Kumaran U. Efficient Liver Segmentation using Advanced 3D-DCNN Algorithm on CT Images. Engineering, Technology & Applied Science Research, 2025, 15(1): 19324–19330.
Pande S D, Kalyani P, Nagendram S, Alluhaidan A S, Badu G H, Ahammad S H, Pandey V K, Kumar A, Sridevi G, Bonyah E. Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer. BMC Medical Imaging, 2025, 25(1): 37–50.
Siale A Y D, Hassan Q M Z, Kadekle M F A S, Veena B S. Enhancing Large-Scale Network Security with a VGG-Net-Based DCNN: A Deep Learning Approach to Anomaly Detection. Journal of Robotics and Control (JRC), 2025, 6(3): 1316–1331.
Adigopula S, Subramanyam M V. DCNN-RFF-NFC: a novel design of NFC security using deep Convolution neural network-based RF fingerprinting. Neural Computing and Applications, 2025, 37(6): 4439–4453.
Dhamale T, Bhandari S, Harpale V, Sakhi P, Napte K, Mahajan A. Autism spectrum disorder detection using parallel DCNN with improved teaching learning optimization feature selection scheme. SAIEE Africa Research Journal, 2025, 116(3): 89–100.
Ye H, Xiao X. MEA-IFE: An Improved Multi-modal Fusion Framework Based on DCNN-BERT-BiLSTM and Its Application in Sentiment Analysis. Information Technology and Control, 2025, 54(2): 451–470.
Xing J, Tian X, Han Y. A Dual-channel Augmented Attentive Dense-convolutional Network for power image splicing tamper detection. Neural Computing and Applications, 2024, 36(15): 8301–8316.
Shi X, Li P, Wu H, Chen Q, Zhu H. A lightweight image splicing tampering localization method based on MobileNetV2 and SRM. IET Image Processing, 2023, 17(6): 1883–1892.
Ding H, Chen L, Tao Q, Fu Z, Dong L, Cui X. DCU-Net: a dual-channel U-shaped network for image splicing forgery detection. Neural computing and applications, 2023, 35(7): 5015–5031.
Alsughayer R, Hussain M, Saeed F, AboalSamh H. Detection and localization of splicing on remote sensing images using image-to-image transformation. Applied Intelligence, 2023, 53(11): 13275–13292.
Zhang H, Guo C, Wang X. Double-branch forgery image detection based on multi-scale feature fusion. Optoelectronics Letters, 2024, 20(5): 307–312.
Saleh A, Mahmoud K, Hussein M M, Nasrat L, Nasser M A. Efficient 2D DCNN approach for detecting and classifying faults in modular power converters. Journal of Integrated Science and Technology, 2025, 13(6): 1137.
Waghumbare A, Singh U. DIAT-DSCNN-GRU-HARNet: A Lightweight DCNN for Video Based Classification of Human Activities. Automatic Control and Computer Sciences, 2025, 59(2): 255–265.
Wang J, Shao H, Peng Y, Liu B. PSparseFormer: Enhancing fault feature extraction based on parallel sparse self-attention and multiscale broadcast feedforward block. IEEE Internet of Things Journal, 2024, 11(13): 22982–22991.
Li C, Zhang J, Niu D, Zhao X, Yang B, Zhang C. Boundary-aware uncertainty suppression for semi-supervised medical image segmentation. IEEE Transactions on Artificial Intelligence, 2024, 5(8): 4074–4086.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Journal of Cyber Security and Mobility