scholarly journals A Robust Image Tampering Detection Method Based on Maximum Entropy Criteria

Entropy ◽  
2015 ◽  
Vol 17 (12) ◽  
pp. 7948-7966 ◽  
Author(s):  
Bo Zhao ◽  
Guihe Qin ◽  
Pingping Liu
Keyword(s):  
Maximum Entropy ◽  
Detection Method ◽  
Tampering Detection ◽  
Image Tampering ◽  
Image Tampering Detection ◽  
Robust Image

scholarly journals Scientific Image Tampering Detection Based On Noise Inconsistencies: A Method And Datasets

2020 ◽  
Author(s):  
Ziyue Xiang ◽  
Daniel Ernesto Acuna
Keyword(s):  
Detection Method ◽  
General Purpose ◽  
Digital Image Forensics ◽  
Image Manipulation ◽  
Tampering Detection ◽  
Scientific Images ◽  
Image Tampering ◽  
Scientific Image ◽  
Image Tampering Detection ◽  
Detection Schemes

Abstract Background: Scientific image tampering is a problem that affects not only authors but also the general perception of the research community. Although previous researchers have developed methods to identify tampering in natural images, these methods may not thrive under the scientific setting as scientific images have different statistics, format, quality, and intentions. Methods: We propose a scientific-image specific tampering detection method based on noise inconsistencies, which is capable of learning and generalizing to different fields of science. We train and test our method on a new dataset of manipulated western blot and microscopy imagery, which aims at emulating problematic images in science. Results: With an average AUC score of 0.927 and an average F1 score of 0.770, it is shown that our method can detect various types of image manipulation in different scenarios robustly. It outperforms other existing general-purpose image tampering detection schemes. Conclusions: The experiment results shows that our method is capable of detecting manipulations in scientific images in a more reliable manner. We discuss applications beyond these two types of images and suggest next steps for making detection of problematic images a systematic step in peer review and science in general. Keywords: Scientific images; Digital image forensics; Noise inconsistency; Scientific image manipulation dataset

Image Tampering Detection Using Convolutional Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 54-63
Author(s):  
Shruti Singhania ◽  
Arju N.A ◽  
Raina Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rapid Growth ◽  
Detection Method ◽  
Research Work ◽  
Optimal Number ◽  
Tampering Detection ◽  
Image Tampering ◽  
Image Tampering Detection ◽  
Journalism Research

Pictures are considered the most reliable form of media in journalism, research work, investigations, and intelligence reporting. With the rapid growth of ever-advancing technology and free applications on smartphones, sharing and transferring images is widely spread, which requires authentication and reliability. Copy-move forgery is considered a common image tampering type, where a part of the image is superimposed with another image. Such a tampering process occurs without leaving any obvious visual traces. In this study, an image tampering detection method was proposed by exploiting a convolutional neural network (CNN) for extracting the discriminative features from images and detects whether an image has been forged or not. The results established that the optimal number of epochs is 50 epochs using AlexNet-based CNN for classification-based tampering detection, with a 91% accuracy.

Sign in / Sign up

Export Citation Format

Share Document