The role of computer vision in combating digital disinformation: automatic detection of deepfakes on social platforms
The role of computer vision in combating digital disinformation: automatic detection of deepfakes on social platforms
DOI:
https://doi.org/10.51473/rcmos.v1i1.2024.1870Keywords:
computer vision; digital disinformation; deepfakes; deep learning; neural networks.Abstract
The dissemination of digital disinformation represents one of the greatest challenges of the connected society. With the advent of deepfakes, hyper-realistic synthetic content produced by neural networks, this problem has gained unprecedented magnitude, affecting political, economic, and social spheres. Computer vision, combined with machine learning, emerges as a strategic tool for the automated identification of such manipulations. This article analyzes, from an interdisciplinary perspective between data science, communication, and technology, the role of computer vision in detecting and mitigating deepfakes on social platforms. Based on recent studies on convolutional neural network architectures, deep learning, and digital forensic approaches, it highlights the importance of interpretable models and strong ethical foundations. Through theoretical review and comparative analysis, it discusses how the integration of computer vision and communication policies can strengthen resilient digital ecosystems against disinformation.
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References
ARAL, SINAN. The hype machine: how social media disrupts our elections, our economy, and our health—and how we must adapt. New York: Currency, 2020.
CHESNEY, ROBERT; CITRON, DANIELLE. Deep fakes: a looming crisis for privacy, democracy, and national security. California Law Review, v. 107, p. 1753–1819, 2019. DOI: https://doi.org/10.2139/ssrn.3213954
DOSHI-VELEZ, FINALE; KIM, BEEN. Towards a rigorous science of interpretable machine learning. arXiv, 2018.
FLORIDI, LUCIANO. The philosophy of information. Oxford: Oxford University Press, 2011. DOI: https://doi.org/10.1002/9781444396836.ch10
FLORIDI, LUCIANO. The logic of information: a theory of philosophy as conceptual design. Oxford: Oxford University Press, 2020. DOI: https://doi.org/10.1093/oso/9780198833635.001.0001
GOODFELLOW, IAN et al. Generative adversarial nets. In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. 2014.
HE, KAIMING et al. Deep residual learning for image recognition. In: IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. 2015. DOI: https://doi.org/10.1109/CVPR.2016.90
HO, JONATHAN et al. Denoising diffusion probabilistic models. In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. 2020.
KARRAS, TERO et al. A style-based generator architecture for generative adversarial networks (StyleGAN). In: IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. 2019. DOI: https://doi.org/10.1109/CVPR.2019.00453
KRIZHEVSKY, ALEX; SUTSKEVER, ILYA; HINTON, GEOFFREY. Imagenet classification with deep convolutional neural networks. In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. 2012.
MATERN, FABIAN; RIESS, CHRISTIAN; STAMMINGER, MARC. Exploiting visual artifacts to expose deepfakes and face manipulations. In: IEEE WINTER APPLICATIONS OF COMPUTER VISION. 2019. DOI: https://doi.org/10.1109/WACVW.2019.00020
PARIS, BRITT; DONOVAN, JOAN. Deepfakes and cheap fakes: the manipulation of audio and visual evidence. Harvard Kennedy School Misinformation Review, 2019.
QIAN, YUCHEN et al. Thinking in frequency: face forgery detection by mining frequency-aware clues. In: EUROPEAN CONFERENCE ON COMPUTER VISION. 2020. DOI: https://doi.org/10.1007/978-3-030-58610-2_6
ROSSLER, ANDREAS et al. FaceForensics++: learning to detect manipulated facial images. In: INTERNATIONAL CONFERENCE ON COMPUTER VISION. 2019. DOI: https://doi.org/10.1109/ICCV.2019.00009
SUN, XIAOYUAN; WANG, LI. Fake retina: detecting deepfakes via biological signal analysis. Journal of Visual Computing, 2020.
SZEGEDY, CHRISTIAN et al. Rethinking the inception architecture for computer vision. In: IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. 2016. DOI: https://doi.org/10.1109/CVPR.2016.308
TANDOC, EDSON; LIM, ZHENG; LING, RICHARD. Defining “fake news”: a typology of scholarly definitions. Digital Journalism, 2018. DOI: https://doi.org/10.1080/21670811.2017.1360143
TUFEKCI, ZEYNEP. Twitter and tear gas: the power and fragility of networked protest. New Haven: Yale University Press, 2018. DOI: https://doi.org/10.1007/s10767-019-9317-2
VACCARI, CRISTIAN; CHADWICK, ANDREW. Deepfakes and disinformation: political campaigns in the AI age. Journal of Political Communication, 2020.
VOSOUGHÍ, SOROUSH; ROY, DEB; ARAL, SINAN. The spread of true and false news online. Science, 2018. DOI: https://doi.org/10.1126/science.aap9559
ZHAO, LIANG et al. DefakeHop: a lightweight high-performance deepfake detector. IEEE Transactions on Multimedia, 2021.
ZUBOFF, SHOSHANA. The age of surveillance capitalism: the fight for a human future at the new frontier of power. New York: PublicAffairs, 2019.
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