Volume no :
1 |
Issue no :
4
Article Type :
Scholarly Article
Author :
K.Rashmi, G Jhanavi, M Shashank Reddy, Sathwik
Published Date :
March, 2025
Publisher :
INTERNATIONAL JOURNAL OF ENGINEERING INNOVATIONS AND MANAGEMENT STRATEGIES
Page No: 1 - 12
Abstract : Deep fake images and videos have emerged as a significant challenge in the digital age, posing threats to privacy, security, and the integrity of information. These synthetic media, generated using advanced deep learning techniques such as generative adversarial networks (GANs) and autoencoders, can convincingly manipulate or fabricate human faces and actions, making it increasingly difficult to distinguish authentic content from manipulated ones. This paper explores the development of an effective deep learning-based framework for the detection of deep fake images and videos, aiming to enhance the reliability and robustness of digital content verification. The proposed approach leverages convolutional neural networks (CNNs) combined with temporal analysis models to capture spatial inconsistencies and temporal artifacts that are often overlooked by human perception but indicative of synthetic manipulations. By training on diverse datasets comprising both real and fake media, the model learns to identify subtle anomalies such as unnatural facial movements, irregular blinking patterns, inconsistent lighting, and texture discrepancies that are characteristic of deep fake generation processes. Moreover, the research integrates attention mechanisms to focus on key facial regions and temporal dynamics, improving detection accuracy across varied scenarios and video qualities. Experimental results demonstrate that the model achieves superior performance compared to traditional handcrafted feature-based detectors and other contemporary deep learning models, maintaining high precision and recall even under adversarial attempts to evade detection. Additionally, the study addresses challenges related to generalization across different deep fake generation techniques and datasets, proposing transfer learning and data augmentation strategies to enhance model adaptability. The importance of real-time detection capabilities is also emphasized, considering the rapid spread of deep fakes on social media and news platforms. Furthermore, ethical considerations and privacy implications are discussed, highlighting the necessity for transparent and responsible deployment of deep fake detection technologies. This work contributes to the growing body of knowledge on multimedia forensics and artificial intelligence by providing a comprehensive and scalable solution for identifying manipulated visual content. Ultimately, the integration of advanced deep learning methodologies for deep fake detection is crucial in safeguarding digital authenticity, preventing misinformation, and fostering trust in multimedia communications in an era increasingly dominated by AI-generated content.
Keyword Deep fake detection, Deep learning, Generative adversarial networks, Convolutional neural networks, Multimedia forensics, Temporal analysis
Reference:
  1. Reddy, N. K., & Murthy, G. V. (2012). Evaluation of Behavioral Security in Cloud Computing. International Journal of Computer Science and Information Technologies, 3(2), 3328-3333.
  2. Murthy, G. V., Kumar, C. P., & Kumar, V. V. (2017, December). Representation of shapes using connected pattern array grammar In 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 819-822). IEEE.
  3. Krishna, K. V., Rao, M. V., & Murthy, G. V. (2017). Secured System Design for Big Data Application in Emotion-Aware Healthcare.
  4. Rani, G. A., Krishna, V. R., & Murthy, G. V. (2017). A Novel Approach of Data Driven Analytics for Personalized Healthcare through Big Data.
  5. Rao, V., Raju, K. S., Murthy, G. V., & Rani, B. K. (2020). Configure and Management of Internet of Things. Data Engineering and Communication Technology, 163.
  6. Ramakrishna, , Kumar, G. K., Reddy, A. M., & Ravi, P. (2018). A Survey on various IoT Attacks and its Countermeasures. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5(4), 143-150.
  7. Chithanuru, V., & Ramaiah, M. (2023). An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions–A review. Concurrency and Computation: Practice and Experience, 35(22), e7724.
  8. Prashanth, J. S., & Nandury, S. V. (2015, June). Cluster-based rendezvous points selection for reducing tour length of mobile element in WSN. In 2015 IEEE International Advance Computing Conference (IACC) (pp. 1230-1235). IEEE.
  9. Kumar, K. A., Pabboju, S., & Desai, N. M. S. (2014). Advance text steganography algorithms: an overview. International Journal of Research and Applications, 1(1), 31-35.
  10. Hnamte, , & Balram, G. (2022). Implementation of Naive Bayes Classifier for Reducing DDoS Attacks in IoT Networks. Journal of Algebraic Statistics, 13(2), 2749-2757.
  11. Balram, G., Anitha, S., & Deshmukh, A. (2020, December). Utilization of renewable energy sources in generation and distribution In IOP Conference Series: Materials Science and Engineering (Vol. 981, No. 4, p. 042054). IOP Publishing.
  12. Subrahmanyam, V., Sagar, M., Balram, G., Ramana, J. V., Tejaswi, S., & Mohammad, H. P. (2024, May). An Efficient Reliable Data Communication For Unmanned Air Vehicles (UAV) Enabled Industry Internet of Things (IIoT). In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) (pp. 1-4). IEEE.
  13. Mahammad, F. S., Viswanatham, V. M., Tahseen, A., Devi, M. S., & Kumar, M. A. (2024, July). Key distribution scheme for preventing key reinstallation attack in wireless networks. In AIP Conference Proceedings (Vol. 3028, No. 1). AIP Publishing.
  14. Lavanya, (2024). In-Cab Smart Guidance and support system for Dragline operator.
  15. Kovoor, M., Durairaj, M., Karyakarte, M. S., Hussain, M. Z., Ashraf, M., & Maguluri, L. P. (2024). Sensor-enhanced wearables and automated analytics for injury prevention in sports. Measurement: Sensors, 32, 101054.
  16. Rao, R., Kovoor, M., Kishor Kumar, G. N., & Parameswari, D. V. L. (2023). Security and privacy in smart farming: challenges and opportunities. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7).
  17. Madhuri, K. (2023). Security Threats and Detection Mechanisms in Machine Learning. Handbook of Artificial Intelligence, 255.
  18. Reddy, A., & Reddy, P. R. S. (2012). Effective data distribution techniques for multi-cloud storage in cloud computing. CSE, Anurag Group of Institutions, Hyderabad, AP, India.
  19. Srilatha, P., Murthy, G. V., & Reddy, P. R. S. (2020). Integration of Assessment and Learning Platform in a Traditional Class Room Based Programming Course. Journal of Engineering Education Transformations, 33, 179-184.
  20. Reddy, P. R. S., & Ravindranadh, K. (2019). An exploration on privacy concerned secured data sharing techniques in cloud. International Journal of Innovative Technology and Exploring Engineering, 9(1), 1190-1198.
  21. Raj, R. S., & Raju, G. P. (2014, December). An approach for optimization of resource management in Hadoop. In International Conference on Computing and Communication Technologies (pp. 1-5).
  22. Ramana, A. V., Bhoga, U., Dhulipalla, R. K., Kiran, A., Chary, B. D., & Reddy, P. C. S. (2023, June). Abnormal Behavior Prediction in Elderly Persons Using Deep Learning. In 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) (pp. 1-5).
  23. Yakoob, S., Krishna Reddy, V., & Dastagiraiah, C. (2017). Multi User Authentication in Reliable Data Storage in In Computer Communication, Networking and Internet Security: Proceedings of IC3T 2016 (pp. 531-539). Springer Singapore.
  24. Sukhavasi, V., Kulkarni, S., Raghavendran, V., Dastagiraiah, C., Apat, S. K., & Reddy, P. S. (2024). Malignancy Detection in Lung and Colon Histopathology Images by Transfer Learning with Class Selective Image Processing.
  25. Dastagiraiah, C., Krishna Reddy, V., & Pandurangarao, K. V. (2018). Dynamic load balancing environment in cloud computing based on VM ware off-loading. In Data Engineering and Intelligent Computing: Proceedings of IC3T 2016 (pp. 483-492). Springer Singapore.
  26. Swapna, N. (2017). „Analysis of Machine Learning Algorithms to Protect from Phishing in Web Data Mining‟. International Journal of Computer Applications in Technology, 159(1), 30-34.
  27. Moparthi, N. R., Bhattacharyya, D., Balakrishna, G., & Prashanth, J. S. (2021). Paddy leaf disease detection using CNN.
  28. Balakrishna, G., & Babu, C. S. (2013). Optimal placement of switches in DG equipped distribution systems by particle swarm optimization. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(12), 6234-6240.
  29. Moparthi, N. R., Sagar, P. V., & Balakrishna, G. (2020, July). Usage for inside design by AR and VR technology. In 2020 7th International Conference on Smart Structures and Systems (ICSSS) (pp. 1-4).
  30. Amarnadh, , & Moparthi, N. R. (2023). Comprehensive review of different artificial intelligence-based methods for credit risk assessment in data science. Intelligent Decision Technologies, 17(4), 1265-1282.
  31. Amarnadh, V., & Moparthi, N. (2023). Data Science in Banking Sector: Comprehensive Review of Advanced Learning Methods for Credit Risk Assessment. International Journal of Computing and Digital Systems, 14(1), 1-xx.
  32. Amarnadh, , & Rao, M. N. (2025). A Consensus Blockchain-Based Credit Risk Evaluation and Credit Data Storage Using Novel Deep Learning Approach. Computational Economics, 1-34.
  33. Shailaja, K., & Anuradha, B. (2017). Improved face recognition using a modified PSO based self- weighted linear collaborative discriminant regression classification. Eng. Appl. Sci, 12, 7234-7241.
  34. Sekhar, P. R., & Goud, S. (2024). Collaborative Learning Techniques in Python Programming: A Case Study with CSE Students at Anurag Journal of Engineering Education Transformations, 38.
  35. Sekhar, R., & Sujatha, B. (2023). Feature extraction and independent subset generation using genetic algorithm for improved classification. Int. J. Intell. Syst. Appl. Eng, 11, 503-512.
  36. Pesaramelli, S., & Sujatha, B. (2024, March). Principle correlated feature extraction using differential evolution for improved classification. In AIP Conference Proceedings (Vol. 2919, No. 1). AIP Publishing.
  37. Tejaswi, , Sivaprashanth, J., Bala Krishna, G., Sridevi, M., & Rawat, S. S. (2023, December). Smart Dustbin Using IoT. In International Conference on Advances in Computational Intelligence and Informatics (pp. 257-265). Singapore: Springer Nature Singapore.
  38. Moreb, , Mohammed, T. A., & Bayat, O. (2020). A novel software engineering approach toward using machine learning for improving the efficiency of health systems. IEEE Access, 8, 23169-23178.
  39. Ravi, P., Haritha, D., & Niranjan, P. (2018). A Survey: Computing Iceberg Queries. International Journal of Engineering & Technology, 7(2.7), 791-793.
  40. Madar, B., Kumar, G. K., & Ramakrishna, C. (2017). Captcha breaking using segmentation and morphological operations. International Journal of Computer Applications, 166(4), 34-38.
  41. Rani, S., & Geetavani, B. (2017, May). Design and analysis for improving reliability and accuracy of big-data based peripheral control through IoT. In 2017 International Conference on Trends in Electronics and Informatics (ICEI) (pp. 749-753). IEEE.
  42. Reddy, T., Prasad, T. S. D., Swetha, S., Nirmala, G., & Ram, P. (2018). A study on antiplatelets and anticoagulants utilisation in a tertiary care International Journal of Pharmaceutical and Clinical Research, 10, 155-161.
  43. Prasad, P. S., & Rao, S. K. M. (2017). HIASA: Hybrid improved artificial bee colony and simulated annealing based attack detection algorithm in mobile ad-hoc networks (MANETs). Bonfring International Journal of Industrial Engineering and Management Science, 7(2), 01-12.
  44. AC, R., Chowdary Kakarla, P., Simha PJ, V., & Mohan, N. (2022). Implementation of Tiny Machine Learning Models on Arduino 33–BLE for Gesture and Speech Recognition.
  45. Subrahmanyam, V., Sagar, M., Balram, G., Ramana, J. V., Tejaswi, S., & Mohammad, H. P. (2024, May). An Efficient Reliable Data Communication For Unmanned Air Vehicles (UAV) Enabled Industry Internet of Things (IIoT). In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) (pp. 1-4). IEEE.
  46. Nagaraj, , Prasad, A. K., Narsimha, V. B., & Sujatha, B. (2022). Swine flu detection and location using machine learning techniques and GIS. International Journal of Advanced Computer Science and Applications, 13(9).
  47. Priyanka, J. H., & Parveen, N. (2024). DeepSkillNER: an automatic screening and ranking of resumes using hybrid deep learning and enhanced spectral clustering approach. Multimedia Tools and Applications, 83(16), 47503-47530.
  48. Sathish, , Thangavel, K., & Boopathi, S. (2010). Performance analysis of DSR, AODV, FSR and ZRP routing protocols in MANET. MES Journal of Technology and Management, 57-61.
  49. Siva Prasad, V. V., Mandapati, S., Kumar Ramasamy, L., Boddu, R., Reddy, P., & Suresh Kumar, B. (2023). Ensemble-based cryptography for soldiers’ health monitoring using mobile ad hoc networks. Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 64(3), 658-671.
  50. Elechi, P., & Onu, K. E. (2022). Unmanned Aerial Vehicle Cellular Communication Operating in Non- terrestrial Networks. In Unmanned Aerial Vehicle Cellular Communications (pp. 225-251). Cham: Springer International Publishing.
  51. Prasad, B. V. V. S., Mandapati, S., Haritha, B., & Begum, M. J. (2020, August). Enhanced Security for the authentication of Digital Signature from the key generated by the CSTRNG method. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1088-1093). IEEE.
  52. Mukiri, R. R., Kumar, B. S., & Prasad, B. V. V. (2019, February). Effective Data Collaborative Strain Using RecTree Algorithm. In Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India.
  53. Balaraju, J., Raj, M. G., & Murthy, C. S. (2019). Fuzzy-FMEA risk evaluation approach for LHD machine–A case study. Journal of Sustainable Mining, 18(4), 257-268.
  54. Thirumoorthi, P., Deepika, S., & Yadaiah, N. (2014, March). Solar energy based dynamic sag compensator. In 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) (pp. 1-6). IEEE.
  55. Vinayasree, P., & Reddy, A. M. (2025). A Reliable and Secure Permissioned Blockchain‐Assisted Data Transfer Mechanism in Healthcare‐Based Cyber‐Physical Systems. Concurrency and Computation: Practice and Experience, 37(3), e8378.
  56. Acharjee, B., Kumar, M., Krishna, G., Raminenei, K., Ibrahim, R. K., & Alazzam, M. B. (2023, May). Securing International Law Against Cyber Attacks through Blockchain Integration. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 2676-2681). IEEE.
  57. Ramineni, , Reddy, L. K. K., Ramana, T. V., & Rajesh, V. (2023, July). Classification of Skin Cancer Using Integrated Methodology. In International Conference on Data Science and Applications (pp. 105- 118). Singapore: Springer Nature Singapore.
  58. LAASSIRI, , EL HAJJI, S. A. Ï. D., BOUHDADI, M., AOUDE, M. A., JAGADISH, H. P., LOHIT,
  59. K., … & KHOLLADI, M. (2010). Specifying Behavioral Concepts by engineering language of RM- ODP. Journal of Theoretical and Applied Information Technology, 15(1).
  60. Prasad, V. R., & Mohanji, Y. K. V. (2021). FACE RECOGNITION-BASED LECTURE

ATTENDANCE SYSTEM: A SURVEY PAPER. Elementary Education Online, 20(4), 1245-1245.

  1. Dasu, R. P., & Gujjari, B. (2015). Technology-Enhanced Learning Through ICT Tools Using Aakash Tablet. In Proceedings of the International Conference on Transformations in Engineering Education: ICTIEE 2014 (pp. 203-216). Springer India.
  2. Reddy, M., Reddy, K. S., Jayaram, M., Venkata Maha Lakshmi, N., Aluvalu, R., Mahesh, T. R., … & Stalin Alex, D. (2022). An efficient multilevel thresholding scheme for heart image segmentation using a hybrid generalized adversarial network. Journal of Sensors, 2022(1), 4093658.
  3. Srinivasa Reddy, K., Suneela, B., Inthiyaz, S., Hasane Ahammad, S., Kumar, G. N. S., & Mallikarjuna Reddy, A. (2019). Texture filtration module under stabilization via random forest optimization methodology. International Journal of Advanced Trends in Computer Science and Engineering, 8(3), 458-469.
  4. Ramakrishna, , Kumar, G. K., Reddy, A. M., & Ravi, P. (2018). A Survey on various IoT Attacks and its Countermeasures. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5(4), 143-150.
  5. Sirisha, , & Reddy, A. M. (2018, September). Smart healthcare analysis and therapy for voice disorder using cloud and edge computing. In 2018 4th international conference on applied and theoretical computing and communication technology (iCATccT) (pp. 103-106). IEEE.
  6. Reddy, A. M., Yarlagadda, S., & Akkinen, H. (2021). An extensive analytical approach on human resources using random forest algorithm. arXiv preprint arXiv:2105.07855.
  7. Kumar, N., Bhavanam, S. N., & Midasala, V. (2014). Image Hiding in a Video-based on DWT & LSB Algorithm. In ICPVS Conference.
  8. Naveen Kumar, G. S., & Reddy, V. S. K. (2022). High performance algorithm for content-based video retrieval using multiple features. In Intelligent Systems and Sustainable Computing: Proceedings of ICISSC 2021 (pp. 637-646). Singapore: Springer Nature Singapore.
  9. Reddy, S., Kumar, G. N., Ritish, B., SaiSwetha, C., & Abhilash, K. B. (2013). Intelligent parking space detection system based on image segmentation. Int J Sci Res Dev, 1(6), 1310-1312.
  10. Naveen Kumar, S., Reddy, V. S. K., & Kumar, S. S. (2018). High-performance video retrieval based on spatio-temporal features. Microelectronics, Electromagnetics and Telecommunications, 433-441.
  11. Kumar, N., & Reddy, M. A. BWT & LSB algorithm based hiding an image into a video. IJESAT, 170- 174.
  12. Lopez, , Sarada, V., Praveen, R. V. S., Pandey, A., Khuntia, M., & Haralayya, D. B. (2024). Artificial intelligence challenges and role for sustainable education in india: Problems and prospects. Sandeep Lopez, Vani Sarada, RVS Praveen, Anita Pandey, Monalisa Khuntia, Bhadrappa Haralayya (2024) Artificial Intelligence Challenges and Role for Sustainable Education in India: Problems and Prospects. Library Progress International, 44(3), 18261-18271.
  13. Yamuna, V., Praveen, R. V. S., Sathya, R., Dhivva, M., Lidiya, R., & Sowmiya, P. (2024, October). Integrating AI for Improved Brain Tumor Detection and Classification. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1603-1609). IEEE.
  14. Kumar, N., Kurkute, S. L., Kalpana, V., Karuppannan, A., Praveen, R. V. S., & Mishra, S. (2024, August). Modelling and Evaluation of Li-ion Battery Performance Based on the Electric Vehicle Tiled Tests using Kalman Filter-GBDT In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
  15. Sharma, , Vij, S., Praveen, R. V. S., Srinivasan, S., Yadav, D. K., & VS, R. K. (2024, October). Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1631-1636). IEEE.
  16. Anuprathibha, T., Praveen, R. V. S., Sukumar, P., Suganthi, G., & Ravichandran, T. (2024, October). Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings. In 2024 Global Conference on Communications and Information Technologies (GCCIT) (pp. 1- 5). IEEE.
  17. Shinkar, A. , Joshi, D., Praveen, R. V. S., Rajesh, Y., & Singh, D. (2024, December). Intelligent solar energy harvesting and management in IoT nodes using deep self-organizing maps. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1-6). IEEE.
  18. Praveen, R. V. S., Hemavathi, U., Sathya, R., Siddiq, A. A., Sanjay, M. G., & Gowdish, S. (2024, October). AI Powered Plant Identification and Plant Disease Classification In 2024 4th

International Conference on Sustainable Expert Systems (ICSES) (pp. 1610-1616). IEEE.

  1. Dhivya, R., Sagili, S. R., Praveen, R. V. S., VamsiLala, P. N. V., Sangeetha, A., & Suchithra, B. (2024, December). Predictive Modelling of Osteoporosis using Machine Learning Algorithms. In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 997-1002). IEEE.
  2. Kemmannu, K., Praveen, R. V. S., Saravanan, B., Amshavalli, M., & Banupriya, V. (2024, December). Enhancing Sustainable Agriculture Through Smart Architecture: An Adaptive Neuro-Fuzzy Inference System with XGBoost Model. In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA) (pp. 724-730). IEEE.
  3. Praveen, V. S. (2024). Data Engineering for Modern Applications. Addition Publishing House