In a digital era where deepfakes pose a significant threat, the need for efficient detection systems is crucial. This study introduces a novel methodology that leverages XAI to identify adversarial attacks on deepfake detectors. By generating interpretability maps, the approach not only detects deepfakes but also enhances understanding of potential vulnerabilities. The research addresses the gap in detecting adversarial attacks on deepfake detectors using XAI-based approaches. The study demonstrates promising results in defending against both known and unknown adversarial attacks, highlighting resilience and versatility across various contexts.
Deepfake technology has advanced significantly, leading to the development of detection methodologies falling into two categories: conventional and end-to-end approaches. However, these detectors are vulnerable to adversarial attacks that aim to deceive or manipulate detector outputs. The integration of XAI techniques plays a crucial role in enhancing model interpretability and robustness against adversarial manipulations.
The experiments conducted evaluate the performance of different XAI methods against various adversarial attacks on deepfake detectors. Results show varying degrees of success depending on the attack type, emphasizing the importance of developing robust defense strategies. The study underscores the criticality of leveraging XAI techniques to enhance model interpretability and fortify model resilience against adversarial manipulations.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Ben Pinhasov... at arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.02955.pdfDeeper Inquiries