Illusion Unveiler

Abstract

Fake image detection is an important problem in the field of computer vision and machine learning. as the use of manipulated images for deception or propaganda purposes is becoming increasingly common. In this, we propose a machine learning approach for detecting fake images, which is based on a combination of deep convolutional neural networks and traditional image processing techniques. Our method extracts a set of features from the input image, including statistical properties, color distributions, and texture information. These features are then fed into a classifier, which determines whether the image is genuine or manipulated. We evaluate our approach on a large data-set of read and fake images and demonstrate that it achieves state-of the art performance in terms of accuracy. precision, and recall. Our results suggest that machine learning methods can be effective for detecting fake images and have the potential to be used in a wide range of applications, including social media content moderation, news verification, and forensic analysis.

Introduction

Fake image detection, or image forensics, is the process of identifying manipulated or falsified digital images. With the widespread use of image editing tools and social media, fake images have become a significant tool for spreading misinformation, propaganda, and fraud. This has led to a growing need for reliable detection methods.

Detection Techniques

• Digital Watermarking: Embeds unique identifiers into images to verify authenticity.

• Error Level Analysis (ELA): Detects inconsistencies in compression quality, indicating possible edits.

• Image Tampering Detection: Analyzes visual clues like unnatural shadows or color inconsistencies to spot manipulation.

These techniques are used by media outlets, social platforms, and law enforcement to verify image authenticity and combat disinformation.

Literature Survey

Recent studies have explored various advanced methods:

1. Zhang et al. (2023): Introduced an attention-based deep learning model to detect DeepFakes by focusing on fine manipulation patterns.

2. Kumar et al. (2022): Developed a multi-modal deep learning approach combining image and metadata for robust forgery detection.

3. Yang et al. (2021): Proposed a self-supervised model that identifies manipulated images without needing labeled data.

4. Nguyen et al. (2020): Analyzed GAN fingerprints to detect images created by generative adversarial networks.

5. Verdoliva (2019): Provided an overview of the tools, challenges, and progress in media forensics and DeepFake detection.

Methodology for Fake Image Detection System

1. Problem Identification: Recognize the growing threat of fake images.

2. Data Collection: Compile a dataset of both real and fake images.

3. Data Preprocessing: Normalize, resize, and augment images.

4. Model Selection: Use a Convolutional Neural Network (CNN) for classification.

5. Model Training: Train the CNN to learn features distinguishing real from fake images.

6. Evaluation: Use metrics like accuracy, precision, recall, and F1-score.

7. System Development: Integrate the model into an application.

8. Testing & Validation: Assess performance with new, unseen images.

9. Deployment (Optional): Build a simple user interface for real-time image verification.

Conclusion

This project focused on detecting fake images using Convolutional Neural Networks (CNNs). Our model effectively learned to distinguish between real and manipulated images, showing promising accuracy. While there\'s scope for further improvement and broader dataset coverage, CNNs proved to be a powerful tool in tackling the challenge of image forgery.

References

[1] Krawetz, N. (2007).Error Level Analysis. In Proceedings of the 2007 International Conference on Digital Forensics andCyber Crime. Retrieved from Hacker Factor. [2] Fridrich, J, &Goljan, M. ( 2003).Digital Image Forensics. IEEE Signal Processing Magazine21(2),8-20. DOI:10.1109/MSP.2003.1185640 [3] Wang, Z, & Bovik, A. C. (2006). Mean Squared Error: Love it or Leave It? |EEE Transactions on Image Processing, 16(1), 23-30. DOI: 10.1 109/TIP.2006.888593 [4] Ghanem, B. & Fawzy, A. (2016).A Novel Image Forgery Detection Method Based on HybridFeatures. International Journal of Computer Applications, 136(6), 1-8. DOI: 10.5120/ijca2016911566 [5] Li, Y., & Hu, J. (2019).Deep Learning for Image Forgery Detection: A Review. Journal of Visual Communication and ImageRepresentation, 58, 324-335. DOI: 10.1016/j;.jvcir.2019.06.020

Copyright

Copyright © 2025 Akshata Bankar, Shruti Jadhav, Advait Bhosale, Prof. Bhaven Doshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.