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Record
Title
SafeDeep : a scalable robustness verification framework for deep neural networks
Type
Conference Proceeding/Paper
Publ. year
2023
Author/s
Baninajjar, Anahita; Hosseini, Kamran; Rezine, Ahmed; Aminifar, Amir
Department/s
LTH Profile Area: Engineering Health; ELLIIT: the Linköping-Lund initiative on IT and mobile communication; Networks and Security; LU Profile Area: Natural and Artificial Cognition; LTH Profile Area: AI and Digitalization; Broadband Communication
In LUP since
2023-03-30
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Total This Year This Month
161 29 0
Downloads per country

Sweden 57 (35%)
United States of America 40 (25%)
China 10 (6%)
Germany 8 (5%)
Hong Kong (China) 5 (3%)
Unknown 4 (2%)
United Kingdom of Great Britain and Northern Ireland 4 (2%)
France 4 (2%)
Pakistan 3 (2%)
Denmark 3 (2%)
Switzerland 3 (2%)
Netherlands (Kingdom of the) 2 (1%)
Iran 2 (1%)
Taiwan (China) 2 (1%)
Finland 2 (1%)
Norway 2 (1%)
India 2 (1%)
Belgium 1 (1%)
Tunisia 1 (1%)
Singapore 1 (1%)
Austria 1 (1%)
Indonesia 1 (1%)
Serbia 1 (1%)
Japan 1 (1%)
Belize 1 (1%)
South Korea 1 (1%)
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The download statistics shown here have been collected since the launch of LUP in October 2007 and are updated every night. Statistics are available for all records with open access fulltexts. Efforts have been made to exclude downloads by robots and track irregular download activities.

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The information on downloads per country is based on the geolocation of IP addresses and may not be completely accurate. The statistics presented here may also change retroactively when the calculation process is improved to provide more accurate results.

Statistics Last Updated
Wed Dec 24 08:25:38 2025