Details

Autor(en) / Beteiligte

• Qiu, Meikang
• Qiu, Han

Titel

Review on Image Processing Based Adversarial Example Defenses in Computer Vision

Ist Teil von

• 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020, p.94-99

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Link zum Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Recent research works showed that deep neural networks are vulnerable to adversarial examples, which are usually maliciously created by carefully adding deliberate and imperceptible perturbations to examples. Several states of the art defense methods are proposed based on the existing image processing methods like image compression and image denoising. However, such approaches are not the final optimal solution for defense adversarial perturbations in DNN models. In this paper, we reviewed two main approaches to deploying image processing methods as a defense. By analyzing and discus!sing the remaining issues, we present two open questions for future research direction including the definition of adversarial perturbations and noises, the novel defense-aware threat model. A further research direction is also given by re-thinking the impacts of adversarial perturbations on all frequency bands.