http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : 아가왈 사우랍 (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
-
A bibliometric study of image steganalysis
Saurabh Agarwal(사우랍 아가왈),Ki-Hyun Jung(정기현) 대한전자공학회 2021 대한전자공학회 학술대회 Vol.2021 No.6
Image steganography is performed to hide secret information into the image. Image steganalysis is applied to detect steganography. In this paper, a bibliometric study is carried out for image steganalysis from 2002 to 2020. A web of science database is used for bibliometric study. Image steganalysis articles are available in science citation index expanded, social sciences citation index, and emerging sources citation index on the web of science, where image steganalysis and image steganography analysis keywords are used.
-
Digital Image Forensics Using Hybrid Feature Set
Saurabh Agarwal(사우랍 아가왈),Ki-Hyun Jung(정기현) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
Digital images are suitable representation of information since one image can depict lots of information. The changes in the image can be done easily with advance technology, sometimes these changes are performed with malicious purpose. In this paper, a robust technique for median filtering detection is proposed. The co—occurrences of difference pairs are calculated in spatial and frequency domain. The performance is evaluated on three diverse databases. The SVM classifier with linear kernel is utilized to classify non—filtered and median filtered images. The experimental analysis is performed to show the proposed method is better with various environment.
-
Forgery Detection Scheme Using Enhanced Markov Model and LBP Texture Operator in Low Quality Images
Saurabh Agarwal(아가왈 사우랍),Ki-Hyun Jung(정기현) 한국정보보호학회 2021 정보보호학회논문지 Vol.31 No.6
본 논문에서는 저품질 이미지에 적용된 미디언 필터링를 검출하는 기법을 제안하고자 한다. 이러한 미디언 필터링 검출은 이미지 포렌식 기법에 사용되고 있는 것으로 제안된 방법에서는 원본 이미지와 미디언 필터링된 이미지를 구분하기 위하여 공간 영역에서 통계적 특징 정보를 추출하고 확장시킨다. 확장된 특징 정보는 마르코프 모델을 사용하고 강인한 특징 집합을 생성하기 위하여 다중 방향 배열을 사용한다. 제안된 방법에서는 검출 정확도를 높이기 위하여 텍스처 연산자를 사용하고 SVM 분류기를 통하여 분류 모델을 훈련시킨다. 실험 결과에서는 JPEG 압축을 사용한 저품질 이미지에서 제안한 방법의 우수함을 보인다. Image forensic is performed to check image limpidness. In this paper, a robust scheme is discussed to detect median filtering in low quality images. Detection of median filtering assists in overall image forensic. Improved spatial statistical features are extracted from the image to classify pristine and median filtered images. Image array data is rescaled to enhance the spatial statistical information. Features are extracted using Markov model on enhanced spatial statistics. Multiple difference arrays are considered in different directions for robust feature set. Further, texture operator features are combined to increase the detection accuracy and SVM binary classifier is applied to train the classification model. Experimental results are promising for images of low quality JPEG compression.
-
A Short Bibliometric Analysis of Image Forgery and Forensics
Saurabh Agarwal(아가왈 사우랍),Ki-Hyun Jung(정기현) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Social networking services frequently allow users to share digital images. With todays technology, altering an image is not difficult, and occasionally, malicious intention is used to accomplish these modifications. Deep fake images and videos are also generated using a generative adversarial network. Image forensics is applied to detect image forgery, image manipulations, and deep fake images. In this paper, a bibliometric study is carried out from the year 2005 to August 2022. The bibliometric analysis makes use of the Web of Science database. The ((digital) AND (image AND (forensic OR forgery OR manipulation)) OR (deep fake)) query is used for the bibliometric study. Articles on these keywords are found on the Web of Sciences science citation index expanded, conference proceeding citation index-science, the social sciences citation index, and the emerging sources citation index.
-
FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features
Dilip Kumar Sharma,Bhuvanesh Singh,아가왈 사우랍,김현성,Raj Sharma 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.1
Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNet-B0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.
-
IoT-Based Automatic Water Quality Monitoring System with Optimized Neural Network
Anusha Bamini A M,Chitra R,아가왈 사우랍,김현성,Punitha Stephan,Thompson Stephan 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.1
One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.
ScienceON연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
