DIGITAL FORGERY IN THE AGE OF MISINFORMATION USING TECHNIQUES FOR RELIABLE IMAGE MANIPULATION DETECTION AND ASSESSING THEIR SOCIETAL IMPACT

Ayushi Tiwari; Kapil Shukla; Krishna Modi

doi:10.29121/digisecforensics.v2.i2.2025.68

Authors

Ms. Ayushi Tiwari National Forensic Sciences University
Kapil Shukla National Forensic Sciences University https://orcid.org/0000-0002-9078-9425
Dr. Krishna Modi National Forensic Sciences University

DOI:

https://doi.org/10.29121/digisecforensics.v2.i2.2025.68

Keywords:

Image Forgery Detection, Metadata, Hashing, Machine Learning, Deep Learning

Abstract

Digital image forgery has become a serious concern in today's information-driven society, as images rapidly circulate across social media, news platforms, and digital communication. As the creation of manipulated images becomes easier and their detection more difficult, the demand for reliable forgery detection techniques has become more urgent than ever. This review covers a wide range of methods that can be used to identify tampered images, with particular attention to metadata verification, hashing-based approaches, and learning-driven strategies. Metadata inspection remains among the simplest and earliest techniques, but it is usually vulnerable because metadata can be easily removed or altered. Hashing-based methods have much stronger robustness by generating unique digital signatures for images. However, they usually fail when minor edits are performed. Machine learning and deep learning techniques have significantly advanced the area, which primarily enables learning complex manipulative patterns automatically. These include convolutional neural networks, attention mechanisms, and hybrid models combining traditional features and deep features for superior detection accuracy. Some of the main focuses of current research are on hybrid architectures aimed at combining strengths for better performance against real-world forgeries, including sophisticated deepfakes. Besides technical advancements, this review highlights the societal importance of image integrity. Reliable forgery detection is important in journalism, forensic analysis, medical imaging, and national security-all those domains where misinformation or tampering could have dire consequences. While tremendous progress has been made, some challenges still remain, particularly with respect to how easily metadata can be tampered with or the realism of AI-generated content. Finally, the paper concludes by identifying future research avenues that have the potential to make forensic systems resilient and help rebuild trust in digital media.

Author Biographies

Ms. Ayushi Tiwari, National Forensic Sciences University

Ms. Ayushi Tiwari is pursuing his M. Sc. Forensic Science (Specialization in Cyber Forensics) at the National Forensic Sciences University, Gandhinagar, Gujarat. She holds a Bachelor’s degree in Forensic Science from the National Forensic Sciences University. She has interned at the Government State of Forensic Science Laboratory, Delhi and In Directorate of forensic science Gandhinagar, gaining experience in Cyber and expertise in UFED , Audio-Video Forensic Analysis.

Kapil Shukla, National Forensic Sciences University

Dr. Kapil Shukla is serving as an Assistant Professor at School of Forensic Science, National Forensic Sciences University, Gandhinagar, Gujarat. He is having 17 years of experience in academics. He has done Ph. D. in the field of Machine Learning. Dr. Kapil Shukla has cleared UGC NET and GSET examination for Lectureship. He is life member of CSI and ISTE.

Dr. Krishna Modi, National Forensic Sciences University

Dr. Krishna Modi is an academician with 7+ years of teaching experience. She has completed her Ph. D. in the field of Machine Learning. She is NET and GATE qualified. Her specialization is Database management Systems, Machine learning, and Data Structure.

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