Generative Adversarial Networks Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Generative Adversarial Networks projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Generative Adversarial Networks Final Year Projects

A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines

This project focuses on detecting fake or misleading online reviews that can mislead customers and harm businesses. It uses a machine learning model called Weighted Support Vector Machine combined with an optimization algorithm called Harris Hawks Optimization to improve accuracy. The method works for multiple languages, including English, Spanish, and Arabic. The system was tested with different techniques and datasets, achieving high accuracy in identifying spam reviews, especially during the COVID-19 pandemic when online reviews increased dramatically.
A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance

This project focuses on detecting rumors on social media more accurately and quickly. It looks at both the hidden meaning in posts and the opinions of users who comment. The system gives more importance to trustworthy users and learns patterns over time. Tests show it can detect rumors faster and better than existing methods.
Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation

This project develops a smart system to detect diseases in chest X-rays. It works by creating a “normal” version of a given X-ray and then highlighting differences between the original and normal images. These differences point to possible diseased areas. The system uses these highlighted areas to improve detection accuracy and is tested on large public X-ray datasets.
Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree

This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
Conditional Generative Adversarial Network Model for Conversion of 2 Dimensional Radiographs into 3 Dimensional Views

This project develops a method to convert 2-D medical images like X-rays into 3-D views. It uses a specialized deep learning model that can show the organ from all angles. The system cleans and standardizes the images before processing, and it is designed to work even with noisy or unclear inputs. Tests on real hospital data show that the generated 3-D images preserve important details and match the quality of the original scans.
Data Augmentation Based on Generative Adversarial Networks for Endoscopic Image Classification

This project aims to help doctors detect digestive system diseases more easily using computer-based image analysis. The system trains several deep learning models to automatically classify diseases from endoscopy images. It also creates extra training images using generative models to improve accuracy. The final model shows strong and safe performance, reducing the workload on medical staff.
Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network

This project focuses on detecting early-stage Alzheimer’s disease using patterns in people’s handwriting captured online. Since there is limited data available, the study uses a special type of artificial intelligence, called DoppelGANger, to generate realistic handwriting examples. These generated examples help train a neural network to recognize Alzheimer’s more accurately. The approach was tested on real handwriting data and showed much better results than existing methods.
Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network

This project studies how cyber attackers can trick smart security systems that protect computer networks. It focuses on poisoning attacks, which feed fake data to these systems to make them fail. The researchers use a type of deep learning called GAN to create realistic fake data and test how well security systems can detect it. Their experiments show that many machine learning models used in network security can be fooled by such attacks.
IoT Network Cybersecurity Assessment With the Associated Random Neural Network

This project develops a smart system to detect hacked devices in an IoT network. It uses a special type of neural network that looks at all devices together instead of checking them one by one. The system learns from real attack data to decide whether each device is safe or compromised. Tests show it works better than older methods.
A Smart Leaf Blow Robot Based on Deep Learning Model

This project created a robot that can automatically collect fallen leaves. It uses a camera and a computer program to recognize leaves on the ground. The robot moves on wheels and directs a blower to gather the leaves into a bag. The system works in real time and can handle different types of leaves without human help.
A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

This project studies how people’s web browsing activity can be tracked and protected. It focuses on website fingerprinting, which identifies the websites a user visits. The research surveys how deep learning can be used both to perform these tracking attacks and to defend against them. It also reviews methods, challenges, and future directions in this area.
A Systematic Review of Facial Expression Detection Methods

This project studies how computers can recognize human emotions from facial expressions. It reviews many research studies that use deep learning techniques, especially convolutional neural networks. The work compares different methods and datasets to see which are most accurate. It helps understand which AI models work best for emotion detection.
Deep Generative Knowledge Distillation by Likelihood Finetuning

This project trains a small model using a larger model without needing real data. Instead, it creates artificial images using a special generator network. The method learns what the teacher model knows and produces samples that help the student model learn well. It aims to match the accuracy of top methods while using fewer generated samples and less time.
Deep Learning-Based Optimization of VisualAuditory Sensory Substitution

This project focuses on helping visually impaired people understand their surroundings using sound instead of sight. The researchers used deep learning to improve a system that converts images into sounds. They tested how changing the system’s settings affects perception and compared the results with human experiments. The study shows that deep learning can make these systems more effective without relying heavily on human testing.
Evolution of Deep Learning-Based Sequential Recommender Systems From Current Trends to New Perspectives

This project studies how modern recommendation systems work. It focuses on systems that learn users’ preferences over time to give better suggestions. The study explains how models like RNNs, CNNs, GANs, GNNs, and transformers are used to understand user behavior. It also looks at methods that handle sparse data to improve recommendations.
Facial Expression Transfer Based on Conditional Generative Adversarial Networks

This project focuses on transferring facial expressions from one face to another using advanced computer vision. It uses a special neural network model that combines key facial features from a source and target face. The model creates realistic images that keep the target person's identity while showing the new expression. Experiments show it works better and faster than previous methods.
Generalization of Forgery Detection With Meta Deepfake Detection Model

This project focuses on detecting fake videos and images created by face manipulation. It uses a deep learning approach that can learn from multiple sources and adapt to new, unseen types of fake media. The model trains in a way that improves its ability to generalize, so it can detect deepfakes without needing updates for each new type. Overall, it aims to make face forgery detection more reliable in real-world situations.
Heterogenous Social Media Analysis For Efficient Deep Learning Fake-Profile Identification.

This project focuses on detecting fake social media accounts. It collects and analyzes data like posts, comments, images, videos, and user activities. The system uses deep learning to find patterns that show an account is fake. Tests show it can identify fake accounts with over 93% accuracy.
Model Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors

This project studies how to make deep learning models focus better on important parts of an image. The researchers use human perception to guide the model’s attention during training. They introduce new ways to reduce randomness in where the model looks, which improves its performance on unfamiliar data. Experiments on synthetic face detection show that models trained this way detect faces more accurately than standard methods.
Modified Red Fox Optimizer With Deep Learning Enabled False Data Injection Attack Detection

This project focuses on protecting modern power systems that use many sensors and produce huge amounts of data. It aims to detect false data attacks that can harm energy efficiency. The method combines deep learning with an optimization algorithm to accurately identify and classify these attacks. Experiments show that this approach works better than existing methods.

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