Results 71 to 80 of about 13,696 (179)

Multi-source forgery detection method based on the fusion of color space features and frequency domain features

网络与信息安全学报
With the rapid development of artificial intelligence technology, image forgery techniques have become increasingly complex and covert, particularly in the areas of generative and image editing forgeries.
Hou Yifan, Miao Zhuang, Wang Jun, Zhang Qi, Li Yang, Wang Jiabao   +5 more
doaj  

Medical Image Forgery Detection

, 2022
Currently, the majority of businesses and organizations rely on internet services because of the improvements in technology and how simple they are to acquire and use. In the medical sector, digital photographs have also gained more significance than before.
openaire   +1 more source

Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

, 2018
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in ...
Bappy, Jawadul H., Bunk, Jason, Chandrasekaran, Shivkumar, Flenner, Arjuna, Manjunath, B. S., Mohammed, Tajuddin Manhar, Nataraj, Lakshmanan, Peterson, Lawrence, Roy-Chowdhury, Amit K.   +8 more
core  

Dual-Module Architecture for Robust Image Forgery Segmentation and Classification Toward Cyber Fraud Investigation

Applied Sciences
This study presents a dual-module architecture for image forgery detection in the context of cyber fraud investigation, designed to provide interpretable and court-admissible forensic evidence.
Donghwan Kim, Hansoo Kim
doaj   +1 more source

CNN-Keypoint Based Two-Stage Hybrid Approach for Copy-Move Forgery Detection

IEEE Access
Authenticating digital images poses a significant challenge due to the widespread use of image forgery techniques, including copy-move forgery. Copy-move forgery involves copying and pasting portions of an image within the same image while applying ...
Anjali Diwan, Anil K. Roy
doaj   +1 more source

Retracted: Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection. [PDF]

Comput Intell Neurosci, 2023
Intelligence And Neuroscience C.
europepmc   +1 more source

Visual Complexity in Korean Documents: Toward Language-Specific Datasets for Deep Learning-Based Forgery Detection

Applied Sciences
Recent advancements in information and communication technology have driven various organizations, including businesses, government agencies, and institutions, to digitize and manage critical documents. Document digitization mitigates spatial constraints
Yong-Yeol Bae, Dae-Jea Cho, Ki-Hyun Jung
doaj   +1 more source

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. [PDF]

Multimed Tools Appl, 2023
Sharma P, Kumar M, Kumar M, Sharma H.
europepmc   +1 more source

Image Forgery Detection using ResNet50

International Journal for Research in Applied Science and Engineering Technology
Abstract: Image forgery detection is crucial in ensuring the integrity of digital media. In this study, we propose a method for detecting image tampering using Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs) with a ResNet50 architecture. Leveraging the CASIA 2.0 Image Tampering Detection Dataset, which consists of authentic (Au) and
Nalluri Brahma Naidu, Thokala Kavyasree, Tadikonda Ravi Teja, Pulimela Sushma Sarayu, Sivangula Sai   +4 more
openaire   +1 more source

Passive approaches for digital image forgery detection. [PDF]

, 2019
Fake images have become widespread in society today. One can find forged images used to sensationalize news, spread political propaganda and rumors, introduce psychological bias, etc. in all forms of media. Claims of image tampering are common in scandals and controversies.
openaire   +2 more sources