Deep Learning: Security and Forensics Research Advances and Challenges

Submission Deadline: 30 October 2019

IEEE Access invites manuscript submissions in the area of Deep Learning: Security and Forensics Research Advances and Challenges.

Generative and discriminative deep learning models have been utilized in a broad range of artificial intelligence-related applications (e.g., computer vision, natural language processing), cybersecurity (e.g., facial authentication, and vulnerability and exploitation detection), and forensic-related tasks. However, cyber attackers could breach the trustworthiness and efficiency of deep learning models (i.e., adversarial machine/deep learning). There are different methods that have been used to hack machine/deep learning models, for example, exploiting the model structure, injecting malicious data in the training, validation, and/or testing sets, and/or modifying hyper-parameters of the models.

The objective of this Special Section in IEEE Access is to compile recent research efforts dedicated to the study of Deep Learning in security and forensic-related applications, to enhance performance in biometrics, spoofing detection, intrusion detection, authentication, digital forensics, access control, image steganography and steganalysis, deep learning computation and training security, and malicious web content identification, etc. Specifically, we are soliciting for high quality and unpublished work on recent advances in new deep learning methodologies that can be applied to a broad range of applications.

The topics of interest include, but are not limited to:

  • Adversarial attacks in deep learning
  • Cryptography protocols and algorithms for deep learning
  • Deep learning computation and training security
  • Deep learning for cyber security applications (e.g., malicious web content identification, intrusion detection and privacy-preserving, vulnerability and exploitation Identification, and facial and/or biometric spoofing detection)
  • Deep learning for natural language processing
  • Deep learning for video and image processing
  • Deep learning-based forensics and anti-forensics
  • Gait authentication and deep learning
  • Generative adversarial deep learning
  • Object detection and transfer learning
  • Privacy and trust challenges associated with deep learning
  • Trends in specific deep learning domains


We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor:  Kim-Kwang Raymond Choo, University of Texas at San Antonio, USA

Guest Editors:

  1. Zhen Qin, University of Electronic Science and Technology of China, China
  2. Nour Moustafa, University of New South Wales @ADFA, Australia
  3. William Bradley Glisson, Sam Houston State University, USA
  4. Sheikh Mahbub Habib, Continental AG, Germany


Relevant IEEE Access Special Sections:

  1. Advanced Software and Data Engineering for Secure Societies
  2. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  3. Trusted Computing

IEEE Access Editor-in-Chief:
  Derek Abbott, Professor, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: