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Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of
  Perturbation and AI Techniques

Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques

22 February 2023
S. Dhesi
Laura Fontes
P. Machado
I. Ihianle
Farhad Fassihi Tash
D. Adama
    AAML
ArXivPDFHTML

Papers citing "Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques"

3 / 3 papers shown
Title
On the Vulnerability of DeepFake Detectors to Attacks Generated by
  Denoising Diffusion Models
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models
Marija Ivanovska
Vitomir Štruc
DiffM
24
10
0
11 Jul 2023
Challenges and Countermeasures for Adversarial Attacks on Deep
  Reinforcement Learning
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
Inaam Ilahi
Muhammad Usama
Junaid Qadir
M. Janjua
Ala I. Al-Fuqaha
D. Hoang
Dusit Niyato
AAML
59
132
0
27 Jan 2020
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
  Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
446
15,639
0
02 Nov 2015
1