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.

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 Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Amit Doegar ◽  
Srinidhi Hiriyannaiah ◽  
G. M. Siddesh ◽  
K. G. Srinivasa ◽  
Maitreyee Dutta
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Research Work ◽  
Network Models ◽  
Neural Network Models ◽  
Tampering Detection ◽  
Image Tampering ◽  
Efficient Detection ◽  
Image Tampering Detection ◽  
Learning Architectures

Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the k -fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.

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

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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