Generative Adversarial Networks Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Generative Adversarial Networks ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.

Generative Adversarial Networks Projects with Source Code

Latest Generative Adversarial Networks Projects

  1. A Deep Learning Based Induced GNSS Spoof Detection Framework
    This project focuses on keeping GPS signals safe from fake or spoofed signals. It uses advanced deep learning methods to learn what normal GPS data looks like. The system can then detect unusual or fake signals, even ones it has not seen before. Tests show it can identify almost all spoofed signals accurately, making GPS systems more secure and reliable.
  2. A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses
    This project develops a new laparoscope for minimally invasive surgery. It uses special liquid lenses to zoom in and focus without moving parts. The system can create 3D images from a single camera in real-time. This helps surgeons see organs and lesions more clearly and judge depth accurately during surgery.
  3. An Introduction to Adversarially Robust Deep Learning
    This project studies how deep learning models, which are used in fields like medical imaging and speech recognition, can be easily fooled by tiny changes in input data. It reviews past research on making these models more reliable. The work explains why previous solutions did not fully succeed. It also points out new directions for improving the safety and robustness of these systems.
  4. Face Generation and Editing With StyleGAN: A Survey
    This project studies how advanced deep learning models can create and change realistic face images. It explains how these models have improved over time and how they can be used to edit faces, restore old images, or change styles. The survey also introduces basic techniques for understanding and using these models. It is designed for students who have some basic knowledge of deep learning.
  5. GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems
    This project studies how advanced AI, called GANs, can trick wireless communication systems. It shows that fake signals made by GANs can look almost exactly like real ones. The research tested this on modern multi-carrier signals used in networks. The results show that receivers can be fooled 99.7% of the time, revealing a serious security risk.
  6. Introducing SPINE: A Holistic Approach to Synthetic Pulmonary Imaging Evaluation Through End-to-End Data and Model Management
    This project focuses on creating and testing artificial chest X-ray images to help doctors diagnose lung diseases. The team developed a method called SPINE to check if these synthetic images are accurate and useful. They compare the fake images with real ones using expert reviews, data analysis, and computer tests. The goal is to make safe and reliable synthetic medical images for research and clinical use.
  7. LAFIT: Efficient and Reliable Evaluation of Adversarial Defenses With Latent Features
    This project studies how deep learning models, especially convolutional neural networks, can be tricked by tiny changes in input that humans cannot notice. The research introduces a new method called LAFIT to test how strong these models are against such attacks. It shows that using hidden information inside the model can make attacks more effective. The work helps make AI systems safer by better evaluating their weaknesses.
  8. Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods
    This project focuses on making Internet of Things (IoT) devices more secure. It uses a smart system to recognize each device by its unique radio signals. The method works without making the devices more complex. Tests show it can detect fake devices and correctly identify real ones with over 90% accuracy.
  9. Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations
    This project focuses on improving eye scans called OCTA, which show the blood vessels in the retina. The researchers created a way to make realistic fake images of these vessels to help train computers. Their method helps the computer find even the smallest blood vessels more accurately. They also shared all their code and data so others can use it.
  10. Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines
    This project focuses on teaching a computer to convert MRI scans of spines into CT-like images. It also measures how confident the computer is about its predictions. By looking at uncertainties, the model can better separate bones from soft tissues. This helps doctors and researchers trust the results more when no expert labels are available.
  11. Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection
    This project uses machine learning to study how common heart-related lab tests change over time in patients. It first learns general patterns from many patients and then applies this knowledge to detect specific heart problems in smaller patient groups. The method improves prediction accuracy and helps doctors better plan tests and treatments. It shows potential for use in other diseases too.
  12. Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation
    This project develops a method to detect diseases in chest X-rays by first creating a “normal” version of each X-ray. The system learns to erase abnormalities using synthetic image pairs. It then finds differences between the original and normal images to highlight disease areas. The method improves detection by training the model with both real and artificially generated images.
  13. At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence
    This project explores how the upcoming 6G wireless networks can benefit from artificial intelligence. It explains how traditional AI methods need large amounts of data, while generative AI can work even with limited or incomplete data. The study reviews different AI models and shows how generative AI can improve network design, security, and communication performance. It also highlights future research directions and challenges in 6G networks.
  14. A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems
    This project focuses on protecting cyber-physical systems, which are systems where computers control real-world devices. These systems are vulnerable to cyber-attacks, which are hard to detect. The project studies how deep learning can be used to identify attacks effectively. It also reviews existing methods and discusses future challenges in this area.
  15. Evasion Attack and Defense on Machine Learning Models in CyberPhysical Systems: A Survey
    This project studies how machine learning in cyber-physical systems can be attacked by hackers. It focuses on a type of attack called evasion attacks, where attackers trick the system by changing data. The work reviews current research on both attacks and defenses and organizes them into clear categories. It also points out gaps and future directions to make these systems safer.
  16. A Methodology and an Empirical Analysis to Determine the Most Suitable Synthetic Data Generator
    This project studies how artificial intelligence can create fake data that looks and behaves like real data. The goal is to find which data generator makes the most realistic and fair datasets while keeping information private. Researchers tested several data generation tools and compared their results on many datasets. They found that CTGAN and PATECTGAN worked best for producing high-quality synthetic data.
  17. A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification
    This project compares traditional image recognition methods with a new approach using quantum computing. It builds a Quantum Convolutional Neural Network to process images faster and more accurately than normal CNNs. The model was tested on image datasets like MNIST and COVIDX-CXR3. The results show that the quantum model improves both speed and accuracy in image classification.
  18. Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework
    This project focuses on improving how computers detect harmful software. It combines traditional methods that use known malware signatures with artificial intelligence to find new, unseen threats. The system uses a special AI model called GAN to create fake safe files, helping the detector learn better. Tests show that this approach detects malware more accurately than existing systems.
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