Deep Learning Based Counter–Forensic Image Classification for Camera Model Identification

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification.

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Structure-Aware Deep Learning for Product Image Classification

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3231742 ◽

2019 ◽

Vol 15 (1s) ◽

pp. 1-20 ◽

Cited By ~ 9

Author(s):

Zhineng Chen ◽

Shanshan Ai ◽

Caiyan Jia

Keyword(s):

Deep Learning ◽

Image Classification ◽

Product Image

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Transfer-Deep Learning Application for Ultrasonic Computed Tomographic Image Classification

2020 International Conference on Control, Automation and Diagnosis (ICCAD) ◽

10.1109/iccad49821.2020.9260569 ◽

2020 ◽

Author(s):

Marwa Fradi ◽

Mouna Afif ◽

El-Hadi Zahzeh ◽

Kais Bouallegue ◽

Mohsen Machhout

Keyword(s):

Deep Learning ◽

Image Classification ◽

Tomographic Image ◽

Computed Tomographic

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Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset

Applied Sciences ◽

10.3390/app11093863 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3863

Author(s):

Ali Emre Öztürk ◽

Ergun Erçelebi

Keyword(s):

Deep Learning ◽

Image Classification ◽

Synthetic Data ◽

Real Data ◽

Corner Detection ◽

Batch Size ◽

Test Accuracy ◽

Classification Problems ◽

Auc Value ◽

Classification Test

A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks.

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