English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning constitutes a pivotal component within the realm of machinelearning, offering remarkable capabilities in tasks ranging from imagerecognition to natural language processing. However, this very strength alsorenders deep learning models susceptible to adversarial examples, a phenomenonpervasive across a diverse array of applications. These adversarial examplesare characterized by subtle perturbations artfully injected into clean imagesor videos, thereby causing deep learning algorithms to misclassify or produceerroneous outputs. This susceptibility extends beyond the confines of digitaldomains, as adversarial examples can also be strategically designed to targethuman cognition, leading to the creation of deceptive media, such as deepfakes.Deepfakes, in particular, have emerged as a potent tool to manipulate publicopinion and tarnish the reputations of public figures, underscoring the urgentneed to address the security and ethical implications associated withadversarial examples. This article delves into the multifaceted world ofadversarial examples, elucidating the underlying principles behind theircapacity to deceive deep learning algorithms. We explore the variousmanifestations of this phenomenon, from their insidious role in compromisingmodel reliability to their impact in shaping the contemporary landscape ofdisinformation and misinformation. To illustrate progress in combatingadversarial examples, we showcase the development of a tailored ConvolutionalNeural Network (CNN) designed explicitly to detect deepfakes, a pivotal steptowards enhancing model robustness in the face of adversarial threats.Impressively, this custom CNN has achieved a precision rate of 76.2% on theDFDC dataset.

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