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نتیجه جستجو - تشخیص جعل

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 A survey: Intelligent system for imposter detection
یک مرور: سیستم هوشمند برای تشخیص جعل کننده-2021
This study aims the impostor is a very cunning person who reaches an obsessive stage to perfection in impersonating someone in actual life, concentrates on his biometric. He analyzes the controls, restrictions, and obstacles that he will face to overcome them. The technologies biometric recognition performs a greatly important role in impostor detection. Biometrics properties refer to the automatic recognition of persons depending on their behavioral and physiological characteristics. Biometrics comprises face recognition, fingerprint, voice recognition, retinal scanning, and so on. Biometrics may increment the reliability of an ID card system. In this paper, a review of the concepts mentioned above will be provided. At first, a presentation about a procedural overview of biometric recognition technologies, ID card systems. Then dissection will be presented for the review of the most recent techniques. A description of each concept will be given and a comparison study is achieved with formal discussion and analysis for each approach result introduces in this study. Finally, a summary of the research results is given.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Face recognition | Voice recognition | Finger print | Biometric systems | ID card | Person identification | Impostor detection | Machine learning | Deep neural networks
مقاله انگلیسی
2 Detection of spoofing attacks for ear biometrics through image quality assessment and deep learning
تشخیص حملات جعلی برای بیومتریک گوش از طریق ارزیابی کیفیت تصویر و یادگیری عمیق-2021
Ear recognition systems are one of the popular person identification systems. These biometric systems need to be protected against attackers. In this paper, a novel method is proposed to detect spoof attacks within ear recognition systems. The proposed method employs Convolutional Neural Network (CNN) which is based on deep learning and Image Quality Measure (IQM) techniques to detect printed photo attacks against ear recognition systems. Full-reference and no-reference image quality measures are used to extract ear image features. Score-level fusion is used to combine the scores obtained from image quality measures. Finally, decision-level fusion is employed to fuse the decisions obtained from CNN and IQM systems. The final decision is obtained as real or fake image as the output of the whole system. The experiments are conducted on publicly available ear datasets namely, AMI, UBEAR, IITD, USTB set 1 and USTB set 2 and the obtained results are compared with the state-of-the-art methods that are focused on printed photo attacks as well.
Keywords: Ear biometrics | Spoof detection | Printed photo attack | Image quality measure | Deep learning
مقاله انگلیسی
3 Machine learning with screens for detecting bid-rigging cartels
یادگیری ماشین با صفحه نمایش برای شناسایی کارتل های تقلب در مزایدات-2019
We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample per- formance of this approach and find it to correctly classify more than 84% of the total of bidding processes as collusive or non- collusive. We also discuss tradeoffs in reducing false positive vs. false negative predictions and find that false negative pre- dictions increase much faster in reducing false positive predic- tions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid-rigging cartels.
Keywords: Bid rigging detection | Screening methods | Machine learning | Lasso | Ensemble methods
مقاله انگلیسی
4 Augmented features to detect image splicing on SWT domain
ویژگی های افزوده شده برای تشخیص برهم خوردن تصویر در دامنه SWT-2019
Nowadays, image forgery is a widespread problem in our lives. Image splicing is one method of forging images, and it has become a common tool for malicious users to modify images. Human eyes cannot perceive forgery operations on forged images; therefore, designing an expert system for detecting image authenticity has become a necessity. The splicing method causes some distortions that can be used to detect forgery on a tampered image. In this work, an expert system is designed that uses the statistical and textural properties of an image in a hybrid manner to detect forgery. The method extracts proper- ties (statistical and textural) from high-level sub-bands of stationary wavelet transform (SWT) domain. SWT is used to obtain image’s details in a multi scale manner in frequency domain. The method extracts statistical features from three sub-bands via Markov model, which generates transition probability ma- trices and then obtains Haralic’s textural features based on gray level co-occurrence matrices. Features from each sub-bands are concatenated to form final feature vector. This expert system is the first in the literature to be designed using textural and statistical features together. Finally, the designed expert sys- tem classifies images as authentic or forged using SVM. The system’s performance was evaluated using three publicly available image databases and tested for different attack types. The experimental results show that using statistical and textural features in a hybrid manner improves detection accuracy when compared to similar works in the literature. The main impact of the proposed system is its high accuracy and robust detection of different attack types.
Keywords: Image splicing detection | Stationary wavelet transform | Markov model | Textural features | Image forgery detection
مقاله انگلیسی
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