POSTER: Towards Understanding the Dynamics of Adversarial Attacks

Yujie Ji, Ting Wang

Research output: Contribution to journalConference articlepeer-review


An intriguing property of deep neural networks (DNNs) is their inherent vulnerability to adversarial inputs, which significantly hinder the application of DNNs in security-critical domains. Despite the plethora of work on adversarial attacks and defenses, many important questions regarding the inference behaviors of adversarial inputs remain mysterious. This work represents a solid step towards answering those questions by investigating the information flows of normal and adversarial inputs within various DNN models and conducting in-depth comparative analysis of their discriminative patterns. Our work points to several promising directions for designing more effective defense mechanisms.

Original languageEnglish (US)
Pages (from-to)2228-2230
Number of pages3
JournalProceedings of the ACM Conference on Computer and Communications Security
StatePublished - 2018
Event25th ACM Conference on Computer and Communications Security, CCS 2018 - Toronto, Canada
Duration: Oct 15 2018 → …

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications


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