ARCHIVES

1. Bird, J.J., Lotfi, A. (2024). CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images. IEEE Access,
Vol 12, pp. 15642-15650. https://doi.org/10.1109/ACCESS.2024.3356789[reference:0]
2. Wang, H., Cheng, R., Zhang, Y., Han, C., Gui, J. (2025). LOTA: Bit-Planes Guided AI-Generated Image Detection. Proceedings of the
IEEE/CVF International Conference on Computer Vision (ICCV), pp. 17246-17255.
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_LOTA_Bit-Planes_Guided_AI-
Generated_Image_Detection_ICCV_2025_paper.html[reference:1]
3. Fu, X., Yan, Z., Yang, Z., Yao, T., Zhao, Y., Ding, S., Li, X. (2025). PiD: Generalized AI-Generated Images Detection with Pixelwise
Decomposition Residuals. Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR 267, pp. 17894-17908.
https://proceedings.mlr.press/v267/fu25i.html[reference:2][reference:3]
4. Zhu, M., Chen, H., Yan, Q., Huang, X., Lin, G., Li, W., Tu, Z., Hu, H., Hu, J., Wang, Y. (2023). GenImage: A Million-Scale Benchmark for
Detecting AI-Generated Image. Advances in Neural Information Processing Systems (NeurIPS) 36, 2024.
https://doi.org/10.48550/arXiv.2306.08571[reference:4].
5. Wang, S.Y., Wang, O., Zhang, R., Owens, A., Efros, A.A. (2020). CNN-Generated Images Are Surprisingly Easy to Spot... for Now.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
https://doi.org/10.48550/arXiv.1912.11035[reference:5][reference:6]
6. Organika. (2024). sdxl-detector. Hugging Face Model Hub. https://huggingface.co/Organika/sdxl-detector[reference:7]
7. Bammey, Q., Meur, R., Gurdjos, P. (2022). Bit-plane Analysis for AI-Generated Image Detection. IEEE International Workshop on
Information Forensics and Security (WIFS).
8. Corvi, R., Cozzolino, D., Verdoliva, L., Poggi, G. (2022). On the Detection of Synthetic Images Generated by Diffusion Models. IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
9. Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Holz, T. (2020). Leveraging Frequency Analysis for Deep Fake Image Recognition.
International Conference on Machine Learning (ICML).
10. Deng, H., Chen, Z., Yin, B. (2024). Universal Detection of AI-Generated Images via Frequency Domain Analysis. IEEE Transactions on
Information Forensics and Security.
11. Neelam LabhadeKumar, Mangala S Biradar, Ashvini Narayan Pawale,"Reinforcement Learning-Based Deep FEFM for Blockchain
Consensus Mechanism Optimization with Non-Linear Analysis"Journal of Computational Analysis and Applications, Vol. 33 No. 05 (2024)
12. Neelam Labhade-Kumar “Shot Boundary Detection Using Artificial Neural Network”, Advances in Signal and Data Processing. Lecture
Notes in Electrical Engineering, Springer, Vol 703. PP-44-55 Jan-2021
13. Neelam Labhade-Kumar Optimizing Cluster Head Selection in Wireless Sensor Networks Using Mathematical Modeling and Statistical
Analysis of The Hybrid Energy-Efficient Distributed (HEED) Algorithm, Communications on Applied Nonlinear Analysis, ISSN: 1074-
133XVol 31 No. 6s (2024), PP-602-617 August 2024
14. Neelam Labhade-Kumar “Experimental Design of Electricity Theft Detection and Alert System Using Arduino Assisted Controller and Smart
Sensors"7th International Conference on Inventive Computation Technologies, IEEE Xplore Part Number : CFP24F70-ART ; ISBN : 979-
8-3503-5929-9, 2024,PP-1961-1968
15. Dr.Neelam Labhade-Kumar “Novel Management Trends Using IOT in Indian Automotive Spares Manufacturing Industries”, Journal of
Pharmaceutical Negative Results , Vol. 13 ISSUE 09,PP 4887-4899, Nov-2022
16. Dr.Neelam Labhade-Kumar “Adaptive Hybrid Bird Swarm Optimization Based Efficient Transmission In WSN”, Journal of Pharmaceutical
Negative Results, Vol. 14 ISSUE 02,PP-480-484, Jan-2023
17. Neelam Labhade-Kumar “Combining Hand-crafted Features and Deep Learning for Automatic Classification of Lung Cancer on CT Scans”,
Journal of Artificial Intelligence and Technology, 202
18. Neelam Labhade-Kumar “Enhancing Crop Yield Prediction in Precision Agriculture through Sustainable Big Data Analytics and Deep
Learning Techniques”, Carpathian Journal of Food Science and Technology,2023, Special Issue, 1-18
19. Neelam Labhade-Kumar “Accident prevention and management system in urban VANET for improving slippery roads ride after rain”
Journal of environmental protection and ecology, ISSN:1311-5065 Issue 2 volume 25,PP 586–599,2024
20. Prof. Dr. Neelam Labhade-Kumar, An image processing method for kidney stone segmentation in CT scan images based on CNN-regularized
extreme learning machine approach, Hybrid and Advanced Technologies, PP- 217-222,202.