FAKE FACE DATABASE AND PREPROCESSING (original) (raw)
Related papers
Effect of Face Tampering on Face Recognition
Signal & Image Processing : An International Journal, 2013
Modern face recognition systems are vulnerable to spoofing attack. Spoofing attack occurs when a person tries to cheat the system by presenting fake biometric data gaining unlawful access. A lot of researchers have originated novel techniques to fascinate these types of face tampering attack. It seems that no comparative studies of different face recognition algorithms on same protocols and fake data have been incorporated. The motivation behind this paper is to present the effect of face tampering on various categories of face recognition algorithms. For this purpose four categories of facial recognition algorithms have been selected to present the obtained results in the form of facial identification accuracy at various tampering and experimental protocols but obtained results are very fluctuating in nature. Finally, we come to the conclusion that it is totally unpredictable to select particular type of algorithm for tampered face recognition.
Facial Verification Along with Spoof Attacks
International Journal of Advanced Research, 2017
Face biometrics assumes an essential part in different authentication applications. Yet, there is a design issues exists to ensure the genuine person along with its originality being alived. For the improvement of such kind of robust framework of face verification along with anitspoofing, the database should include three kinds of data i.e. Genuine, Fake and Imposter. In this paper, a database is designed to work for face verification and anti-spoofing technique. The Local Binary Pattern is adopted to extract the features and calculate the scores for genuine, fake and imposter attacks. This research would provide a more realistic and challenging platform for facial anti-spoofing and verification research.
Masquerade Attack Analysis for Secured Face Biometric System
IJRTE, 2021
Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day's biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users' artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today's world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.
2017
In biometric verifying the identity is notable problem is to promise the actual presence of a real authentic trait opposite to a unlawful self-regulated artificial or reconstructed item, which requires the build out an innovative and effective protection methods. In this proposed method, we present a fable software-based fake classification method which can be used in multiple biometric techniques to identify various means of dishonest attack attempts. The objective of the proposed system is by adding liveness evaluation in a rapid, convivial, and non-obtrusive way, through the use of image quality assessment improve the safety of biometric official acceptance structures. The implemented work presents a high degree of simplicity, that makes this system appropriate for the applications that responds to events or signals within a predictable time, 10 common image attribute are extracted or abstracted from the images acquired in the database to distinguish atween authentic and fake ite...
A Review on Face Anti-Spoofing
IJITEE (International Journal of Information Technology and Electrical Engineering), 2021
The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.
To detect duplication of identity during authentication of online payment on mobile or personal computer, the automatic face recognition is widely used now days. The biometric presentation attacks can be performed to gain access to these systems. It is performed by presenting the authorized person"s photo or video. Hence it is important to study the various face spoof attacks. Currently proposed face spoof detection techniques have less generalization ability as these are not considering all factors and do not detect the spoofing medium.The four features such as specular reflection, blurriness, chromatic moment and color diversity are used to analyze the image distortion. The different classifiers are trained for printed photo attack and video replay attack to differentiate between genuine and spoof faces. We also propose an approach to detect the spoofing medium by checking the boundary of the captured image during the photo attack and video attack and an approach to detect the blinking of eye for detecting liveness. It gives us high efficiency rather than existing methods.
Face Spoofing and Counter-Spoofing: A Survey of State-of-the-art Algorithms
Transactions on Machine Learning and Artificial Intelligence, 2017
In the current scenario of biometric-based identity verification, a face is still being proved to be an essential physiological evidence for successful person identification without letting know the target. Nevertheless, repeated attacks of intruders can cause the face recognition system insecure because of easy availability of face images or pictures of a person in social networks or in other networked resources. Spoofing facial identity in a biometric system is not a difficult task for intruders. When an intruder presents a photograph or a video containing a face of a person in front of a networked camera which is integrated with a face biometric system, spoofing is referred to as presentation attack. Without anti-spoofing mechanisms, biometric systems are at high risk in case of susceptible attacks. Thus, detecting face spoof in a face biometric system is a challenging research field. The aim of this book is to summarize some of the most popular face spoof detection techniques wh...
A Case Study on Face Spoof Detection
IRJET, 2022
User authentication is a vital step in protecting information, and facial bio metrics might assist in this regard. Face bio metrics seems to be more natural, simple to use, and less intrusive to humans. Unfortunately, emerging research has revealed that face bio metrics are extremely sensitive to spoofing assaults. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. This report discusses many types of assaults against visual spectrum facial recognition systems. We propose comprehensive data sets for assessing the susceptibility of recognition systems and the effectiveness of countermeasures. Finally, we give a brief overview of anti-spoofing strategies for visual spectrum face identification, as well as a viewpoint on difficulties that remain unresolved.
Fraudulent Face Image Detection
ITM Web of Conferences, 2020
Due to the growing advancements in technology, many software applications are being developed to modify and edit images. Such software can be used to alter images. Nowadays, an altered image is so realistic that it becomes too difficult for a person to identify whether the image is fake or real. Such software applications can be used to alter the image of a person’s face also. So, it becomes very difficult to identify whether the image of the face is real or not. Our proposed system is used to identify whether the image of a face is fake or real. The proposed system makes use of machine learning. The system makes use of a convolution neural network and support vector classifier. Both these machine learning models are trained using real as well as fake images. Both these trained models will take an image as an input and will determine whether the image is fake or real.
Face Spoofing Detection Techniques using Biometrics
The modern biometric technologies provides us better convenience and security features. Face Recognition Biometric systems are being used and deployed in applications such as surveillance, forensic investigation etc., but it is vulnerable mostly in case of face spoofing attacks. Such spoofing can be done by means of video frames, printed photo. To detect these types of attacks, the liveness of face detection is being developed, and also being deployed in face recognition biometric systems. If these methods don't exist in the face recognition biometric systems, it may give permission to a malicious person to masquerade as authentic users to the data file system. To address these problems, it's important to develop a secure biometric recognition system. The current method and approach to detect the liveness within the facial biometrics by making use of the feature extraction methods, includes Local Binary Pattern (LBP), Color Moment Features (CMF). In the proposed system combining two or three features proposed mainly, Histogram of Oriented Gradients (HOG), Spectral Information Divergence (SID), Binarized Statistical Image Features (BSIF), Weber Local Descriptor (WLD) and Local Phase Quantization (LPQ). Support Vector Machine (SVM) classifier gives the result as whether the image is spoofed or real. Done detailed survey on face spoof detection methods, feature methods and algorithms that are existing today and being used for the detection of spoof images. Based on the facts gathered, the execution with minimum and simple use of hardware makes biometric systems better secured and robust.