TY - CHAP
T1 - Forgery detection based on deep learning for smart systems
T2 - Recent advances and collection of datasets
AU - Gaurav, Akshat
AU - Gupta, Brij B.
AU - Bansal, Shavi
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2025 selection and editorial matter, Ahmed A Abd El-Latif, Lo'ai Tawalbeh, Manoranjan Mohanty, Brij B Gupta and Konstantinos E Psannis. All rights reserved.
PY - 2024/10/7
Y1 - 2024/10/7
N2 - In this digital era, most information is in the digital format. This leads to a new type of cyber-attack known as digital forgery. Most of the forgery techniques attack the integrity of the digital asset. Due to the heterogeneity of digital content, it is difficult to detect forgery attacks using traditional approaches. In this context, we present forgery detection techniques related to smart systems. This chapter focuses on deep learning-based forgery detection models and will help young researchers to understand the digital forgery detection approach.
AB - In this digital era, most information is in the digital format. This leads to a new type of cyber-attack known as digital forgery. Most of the forgery techniques attack the integrity of the digital asset. Due to the heterogeneity of digital content, it is difficult to detect forgery attacks using traditional approaches. In this context, we present forgery detection techniques related to smart systems. This chapter focuses on deep learning-based forgery detection models and will help young researchers to understand the digital forgery detection approach.
UR - http://www.scopus.com/inward/record.url?scp=85204999609&partnerID=8YFLogxK
U2 - 10.1201/9781003207573-10
DO - 10.1201/9781003207573-10
M3 - Chapter
AN - SCOPUS:85204999609
SN - 9781032075396
SP - 196
EP - 210
BT - Digital Forensics and Cyber Crime Investigation
ER -