Search results for: Ji-Won Han
2 Anonymous Editing Prevention Technique Using Gradient Method for High-Quality Video
Authors: Jiwon Lee, Chanho Jung, Si-Hwan Jang, Kyung-Ill Kim, Sanghyun Joo, Wook-Ho Son
Abstract:Since the advances in digital imaging technologies have led to development of high quality digital devices, there are a lot of illegal copies of copyrighted video content on the Internet. Also, unauthorized editing is occurred frequently. Thus, we propose an editing prevention technique for high-quality (HQ) video that can prevent these illegally edited copies from spreading out. The proposed technique is applied spatial and temporal gradient methods to improve the fidelity and detection performance. Also, the scheme duplicates the embedding signal temporally to alleviate the signal reduction caused by geometric and signal-processing distortions. Experimental results show that the proposed scheme achieves better performance than previously proposed schemes and it has high fidelity. The proposed scheme can be used in unauthorized access prevention method of visual communication or traitor tracking applications which need fast detection process to prevent illegally edited video content from spreading out.
Keywords: Editing prevention technique, gradient method, high-quality video, luminance change, visual communication.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1796
1 Improving Similarity Search Using Clustered Data
Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong
This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.
Keywords: Visual search, deep learning, convolutional neural network, machine learning.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 691