Deep learning and security have made remarkable progress in the last years. On the one hand, neural networks have been recognized as a promising tool for security in academia and industry. On the other hand, the security of deep learning has gained focus in research, the robustness, privacy, and interpretability of neural networks has recently been called into question.
This workshop strives for bringing these two complementary views together by (a) exploring deep learning as a tool for security as well as (b) investigating the security and privacy of deep learning.
DLS seeks contributions on all aspects of deep learning and security. Topics of interest include (but are not limited to):
We accept two types of submissions:
The submitted paper can be up to six pages, plus additional references. To be considered, papers must be received by the submission deadline (see Important Dates).
Papers must be formatted for US letter (not A4) size paper. The text must be formatted in a two-column layout, with columns no more than 9.5 in. tall and 3.5 in. wide. The text must be in Times font, 10-point or larger, with 11-point or larger line spacing. Authors are strongly recommended to use the latest IEEE conference proceedings templates. Failure to adhere to the page limit and formatting requirements are grounds for rejection without review. Submissions must be in English and properly anonymized.
For any questions, contact the workshop organizers at firstname.lastname@example.org
All accepted submissions will be presented at the workshop. The archival papers will be included in the IEEE workshop proceedings. Due to time constraints, accepted papers will be selected for presentation as either talk or poster based on their review score and novelty. Nonetheless, all accepted papers should be considered as having equal importance.
One author of each accepted paper is required to attend the workshop and present the paper for it to be included in the proceedings.