69,948 research outputs found
Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques
Deep learning constitutes a pivotal component within the realm of machine
learning, offering remarkable capabilities in tasks ranging from image
recognition to natural language processing. However, this very strength also
renders deep learning models susceptible to adversarial examples, a phenomenon
pervasive across a diverse array of applications. These adversarial examples
are characterized by subtle perturbations artfully injected into clean images
or videos, thereby causing deep learning algorithms to misclassify or produce
erroneous outputs. This susceptibility extends beyond the confines of digital
domains, as adversarial examples can also be strategically designed to target
human cognition, leading to the creation of deceptive media, such as deepfakes.
Deepfakes, in particular, have emerged as a potent tool to manipulate public
opinion and tarnish the reputations of public figures, underscoring the urgent
need to address the security and ethical implications associated with
adversarial examples. This article delves into the multifaceted world of
adversarial examples, elucidating the underlying principles behind their
capacity to deceive deep learning algorithms. We explore the various
manifestations of this phenomenon, from their insidious role in compromising
model reliability to their impact in shaping the contemporary landscape of
disinformation and misinformation. To illustrate progress in combating
adversarial examples, we showcase the development of a tailored Convolutional
Neural Network (CNN) designed explicitly to detect deepfakes, a pivotal step
towards enhancing model robustness in the face of adversarial threats.
Impressively, this custom CNN has achieved a precision rate of 76.2% on the
DFDC dataset
Towards a collocation writing assistant for learners of Spanish
This paper describes the process followed in creating a tool aimed at helping learners produce collocations in Spanish. First we present the Diccionario de colocaciones del español (DiCE), an online collocation dictionary, which represents the first stage of this process. The following section focuses on the potential user of a collocation learning tool: we examine the usability problems DiCE presents in this respect, and explore the actual learner needs through a learner corpus study of collocation errors. Next, we review how collocation production problems of English language learners can be solved using a variety of electronic tools devised for that language. Finally, taking all the above into account, we present a new tool aimed at assisting learners of Spanish in writing texts, with particular attention being paid to the use of collocations in this language
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