원문정보
초록
영어
As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.
목차
Ⅰ. Introduction
Ⅱ. Conceptual Background
2.1. Discrete Cosine Transform
2.2. Image Attacks
2.3. DCT-Based Feature Extraction Techniques
2.4. Evaluation Metrics
Ⅲ. Related Work
3.1. DCT-Based Techniques
3.2. Some Other Techniques
3.3. Novelty of the Research
Ⅳ. Proposed Method
4.1. Proposed Method Details
4.2. Method Implementation
Ⅴ. Experimental Results
5.1. Experiment 1: Finding Attack Patterns to Features Relations
5.2. Experiment 2: Attack Detection Using Neural Networks
5.3. Result Comparison: Robustness-Sensitivity and Machine Learning Experiments
Ⅵ. Concluding Remarks
6.1. Contributions of Our Work