Towards realism in drawing areas of interest on architecture diagrams

By W. Jernigan, A. Horvath, M. Lee, Margaret Burnett, Taylor Cuilty, S. Kuttal, Anicia Peters, Irwin Kwan, Faezeh Bahmani, Ko and 2 more. 2009

162 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Information Systems, Software Engineering

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Citations in the corpus

Security Evaluation of Support Vector Machines in Adversarial Environments (2014, background): “esponding attacks on the targeted classiﬁer; e.g., [4, 5, 18, 36, 40, 41, 42, 46]. The second explores the development of proper countermeasures and learning algorithms robust to known attacks; e.g., [26, 41, 57]. Although some prior work does address aspects of the empirical evaluation of classiﬁer”
Support vector machines under adversarial label contamination (2015, methodology): “, using game-theoretical models for classification [15, 20–22], probabilistic models of the data distribution drift under attack [23, 24], and even multiple classifier systems [25–27].”
Multibiometric Cryptosystems Based on Feature-Level Fusion (2012, background): “One of the limitations of a multibiometric system is that it is possible for an adversary to get successfully authenticated by spoofing only a subset of the involved biometric traits [43].”
Towards anomaly detection for increased security in multibiometric systems: Spoofing-resistant 1-median fusion eliminating outliers (2014, background): “Several previous research projects have indicated the vulnerability of multimodal biometric systems against single or multiple biometric spoofing attacks [15, 14, 3, 1].”
Multimodal fusion vulnerability to non-zero effort (spoof) imposters (2010, methodology): “Enhanced fusion strategies such as proposed here and in Rodrigues, et al [9], need to fully consider the tradeoff in FRR, FAR, and newly introduced SFAR, to utilize the power of multimodal fusion for increased performance and security.”
Face Anti-spoofing: Visual Approach (2014, background): “it has been recently shown [3] that multimodal systems, when naively tuned, can be intrinsically less secure than unimodal ones.”
Design of robust classifiers for adversarial environments (2011, methodology): “To estimate the class-conditional distributions of the matching scores, we made the usual assumption of conditional independence between the matching scores given Y , and computed an MLE of P (x1, x2|Y ) from TR, using a product of Gamma distributions, as in [3].”
Multiple classifier systems for robust classifier design in adversarial environments (2010, background): “Some opposite evidence was indeed provided in [45] for multi-modal biometric systems.”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “Our ultimate goal is to provide some useful guidelines for improving the security of pattern recognition in adversarial settings, and to suggest related open issues to foster research in this area.”
Robustness of multi-modal biometric verification systems under realistic spoofing attacks (2011, methodology): “In this paper we adopted the score-level fusion scheme, as in [12, 13, 15].”
Motion-based counter-measures to photo attacks in face recognition (2014, background): “However, it has been recently shown [3] that multimodal systems, if naively tunned, are intrinsically less secure than unimodal ones.”