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Anti-spoofing, Voice Databases

2014
As with any task involving statistical pattern recognition, the assessment of spoofing and anti-spoofing approaches for voice recognition calls for significantscale databases of spoofed speech signals. Depending on the application, these signals should normally reflect spoofing attacks performed prior to acquisition at the sensor or microphone.
Federico Alegre   +4 more
openaire   +1 more source

Anti-spoofing: Face Databases

2014
Datasets for the evaluation of face verification system vulnerabilities to spoofing attacks and for the evaluation of face spoofing countermeasures.
Anjos, André   +2 more
openaire   +2 more sources

Anti-spoofing, Voice Conversion

2014
Voice conversion is a process which converts or transforms one speaker’s voice towards that of another. The literature shows that voice conversion can be used to spoof or fool an automatic speaker verification system. State-of-the-art voice conversion algorithms can produce high-quality speech signals in real time and are capable of fooling both human ...
Nicholas Evans   +3 more
openaire   +1 more source

Anti-spoofing: Evaluation Methodologies

2014
Following the definition of the task of the anti-spoofing systems to discriminate between real accesses and spoofing attacks, anti-spoofing can be regarded as a binary classification problem. The spoofing databases and the evaluation methodologies for anti-spoofing systems most often comply to the standards for binary classification problems.
Chingovska, Ivana   +2 more
openaire   +2 more sources

Anti-spoofing: Iris Databases

2014
Anti-spoofing may be defined as the pattern recognition problem of automatically differentiating between real and fake biometric samples produced with a synthetically manufactured artifact (e.g., iris photograph or plastic eye). As with any other machine learning problem, the availability of data is a critical factor in order to successfully address ...
Javier Galbally, Anna Bori Toth
openaire   +1 more source

Context based face anti-spoofing

2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2013
The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks and the number of countermeasures is gradually increasing. Fairly good results have been reported on the publicly available databases but it is reasonable to assume that there exists no superior anti-spoofing technique due to the ...
Komulainen Jukka   +2 more
openaire   +1 more source

Iris Anti-spoofing

2014
Iris images contain rich texture information for reliable personal identification. However, forged iris patterns may be used to spoof iris recognition systems. This paper proposes an iris anti-spoofing approach based on the texture discrimination between genuine and fake iris images. Four texture analysis methods include gray level co-occurrence matrix,
Zhenan Sun, Tieniu Tan
openaire   +1 more source

Curvelet-based fingerprint anti-spoofing

Signal, Image and Video Processing, 2009
Existing perspiration-based liveness detection algorithms need two successive images (captured in certain time interval), hence they are slow and not useful for real-time applications. Liveness detection methods using extra hardware increase the cost of the system.
Shankar Bhausaheb Nikam, Suneeta Agarwal
openaire   +1 more source

Toward Incentivizing Anti-Spoofing Deployment

IEEE Transactions on Information Forensics and Security, 2014
IP spoofing-based flooding attacks are a serious and open security problem on the current Internet. The best current antispoofing practices have long been implemented in modern routers. However, they are not sufficiently applied due to the lack of deployment incentives, i.e., an autonomous system (AS) can hardly gain additional protection by deploying ...
Bingyang Liu   +2 more
openaire   +1 more source

A deployable approach for inter-AS anti-spoofing

2011 19th IEEE International Conference on Network Protocols, 2011
Filtering IP packets with spoofed source addresses not only improves network security, but also helps with network diagnosis and management. Compared with filtering spoofing packets at the edge of network which involves high deployment and maintenance cost, filtering at autonomous system (AS) borders is more cost-effective.
Bingyang Liu, Jun Bi, Yu Zhu
openaire   +1 more source

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