Deepfake | Sociamonials Blog

Deepfake

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Deepfake technology, a rapidly progressing field, specializes in generating forged yet extremely convincing images, videos, or audio clips. With its roots tracing back to the 19th-century photo alteration, the technology took a significant leap forward in the digital video^[1] era of the 1990s. Through the application of techniques such as Generative Adversarial Networks (GANs), deepfakes have reached an unprecedented level of realism. Yet, they come with their share of disputes, including ethical concerns, particularly in the realm of pornography, and the risk of being utilized for spreading disinformation^[2], leading to discussions about regulatory measures. Despite the controversies, deepfakes have found commendable uses in sectors like entertainment, where they’re employed for visual effects and de-ageing roles, and in corporate training, where they enable the creation of customized videos. However, as the evolution of deepfakes persists, the dialogue about their societal influence and the potential for exploitation remains ongoing.

Terms definitions

1. ↑ digital video. The primary focus of this passage, digital video, is a digital recording method that operates by employing a digital signal instead of an analog video signal. The inception of this technology took place when MOS image sensors were incorporated into digital video cameras. Since its inception, it has evolved significantly, marked by the creation of the first semiconductor image sensor, CCD, and the entertainment industry's transition to digital imaging. Presently, digital video is extensively employed in a variety of sectors, such as entertainment, education, and research. It also finds use in diverse applications such as surveillance, storage, and monitoring vital signs in the healthcare industry. One of the defining characteristics of digital video is its capacity for effortless duplication and distribution without any loss of quality. It also provides various storage alternatives, including Blu-ray Discs, data storage devices, and internet streaming. Its technical elements encompass bandwidth usage for live videos and storage usage for recorded videos, with compression significantly diminishing data usage. There are also distinct video formats for consumer and professional utilization. The highest resolution of digital video exhibited so far is 132.7 megapixels.

2. ↑ disinformation. Disinformation, a term rooted in the Proto-Indo-European language family, is the deliberate propagation of inaccurate or misleading data, typically for political or sociocultural manipulation. This practice gained prominence in the 1980s and has been the focus of comprehensive research to decipher its origins, techniques, and effects. Disinformation is frequently employed in deceptive strategies on social platforms and is distinct from misinformation and malinformation. It's prevalent in political contexts, often muddling citizens and disheartening their participation. Disinformation has worldwide consequences, utilized by governments, NGOs, and global businesses. It poses a threat to the integrity of elections and can instigate societal rifts. Entities like NATO and the EU have implemented various strategies to tackle this problem. The exploration of disinformation also encompasses ethical aspects and its application in warfare. Despite these initiatives, disinformation continues to be a persistent issue due to its ubiquitous presence and the challenge in gauging its real impact.

Deepfake (Wikipedia)

It has been suggested that this article be merged with Synthetic media. (Discuss) Proposed since January 2024.

Deepfakes (portmanteau of "deep learning" and "fake") are synthetic media that have been digitally manipulated to replace one person's likeness convincingly with that of another. It can also refer to computer-generated images of human subjects that do not exist in real life. While the act of creating fake content is not new, deepfakes leverage tools and techniques from machine learning and artificial intelligence, including facial recognition algorithms and artificial neural networks such as variational autoencoders (VAEs) and generative adversarial networks (GANs). In turn the field of image forensics develops techniques to detect manipulated images.

Deepfakes have garnered widespread attention for their potential use in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financial fraud. The spreading of disinformation and hate speech through deepfakes has a potential to undermine core functions and norms of democratic systems by interfering with people's ability to participate in decisions that affect them, determine collective agendas and express political will through informed decision-making. This has elicited responses from both industry and government to detect and limit their use.

From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing the disruption of the entertainment and media industries.

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