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pro vyhledávání: '"Barshan, Elnaz"'
In this work, we present an efficient multi-bit deep image watermarking method that is cover-agnostic yet also robust to geometric distortions such as translation and scaling as well as other distortions such as JPEG compression and noise. Our design
Externí odkaz:
http://arxiv.org/abs/2206.10813
In this work, we focus on the use of influence functions to identify relevant training examples that one might hope "explain" the predictions of a machine learning model. One shortcoming of influence functions is that the training examples deemed mos
Externí odkaz:
http://arxiv.org/abs/2003.11630
While progress has been made in crafting visually imperceptible adversarial examples, constructing semantically meaningful ones remains a challenge. In this paper, we propose a framework to generate semantics preserving adversarial examples. First, w
Externí odkaz:
http://arxiv.org/abs/1903.03905
A promising paradigm for achieving highly efficient deep neural networks is the idea of evolutionary deep intelligence, which mimics biological evolution processes to progressively synthesize more efficient networks. A crucial design factor in evolut
Externí odkaz:
http://arxiv.org/abs/1704.02081
Publikováno v:
Electronic Imaging. 35:376-1
In this work, we present an efficient multi-bit deep image watermarking method that is cover-agnostic yet also robust to geometric distortions such as translation and scaling as well as other distortions such as JPEG compression and noise. Our design
Publikováno v:
2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP); 2015, p1-6, 6p
Autor:
Barshan, Elnaz, Fieguth, Paul
Publikováno v:
2014 IEEE International Conference on Image Processing (ICIP); 2014, p2754-2758, 5p
Publikováno v:
SID Symposium Digest of Technical Papers; Jun2015, Vol. 46 Issue 1, p514-517, 4p
Akademický článek
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Publikováno v:
Progress in Pattern Recognition, Image Analysis, Computer Vision & Applications; 2009, p209-216, 8p