Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust. With the increasing accessibility of generative AI, tools for voice cloning, face-swapping, and synthetic media creation have advanced significantly, lowering both financial and technical barriers for their use. While these technologies present innovative opportunities, their rapid growth raises concerns about trust, privacy, and security. This white paper explores the implications of deepfake technology, analyzing its role in enabling fraud, misinformation, and the erosion of authenticity in multimedia. Using cost-effective, easy to use tools such as Runway, Rope, and ElevenLabs, we explore how realistic deepfakes can be created with limited resources, demonstrating the risks posed to individuals and organizations alike. By analyzing the technical and ethical challenges of deepfake mitigation and detection, we emphasize the urgent need for regulatory frameworks, public awareness, and collaborative efforts to maintain trust in digital media.

Artificial intelligence "nudification" apps that create deepfake sexual images of children should be immediately banned, amid growing fears among teenage girls that they could fall victim, the children's commissioner for England is warning. Girls said they were stopping posting images of themselves on social media out of a fear that generative AI tools could be used to digitally remove their clothes or sexualise them, according to the commissioner's report on the tools, drawing on children's experiences. Although it is illegal to create or share a sexually explicit image of a child, the technology enabling them remains legal, the report noted. "Children have told me they are frightened by the very idea of this technology even being available, let alone used. They fear that anyone – a stranger, a classmate, or even a friend – could use a smartphone as a way of manipulating them by creating a naked image using these bespoke apps," the commissioner, Dame Rachel de Souza, said.

Artificial intelligence (AI) is producing hyperrealistic "digital twins" of politicians, celebrities, pornographic material, and more – leaving victims of deepfake technology struggling to determine legal recourse. Former CIA agent and cybersecurity expert Dr. Eric Cole told Fox News Digital that poor online privacy practices and people's willingness to post their information publicly on social media leaves them susceptible to AI deepfakes. "The cat's already out of the bag," he said. "They have our pictures, they know our kids, they know our family. They know where we live. And now, with AI, they're able to take all that data about who we are, what we look like, what we do, and how we act, and basically be able to create a digital twin," Cole continued.

Individuals from minority groups, even with equivalent qualifications, consistently receive fewer opportunities in critical areas such as employment, education, and healthcare. Yet, empirically demonstrating the existence of such pervasive bias, let alone measuring the extent of bias or correcting it, remains a significant challenge. Over several decades, researchers have utilized a range of experimental methodologies to test for biases in real-life situations (Bertrand and Duflo 2017). Audit studies, among the earliest of such methods, match two individuals who are similar in all respects except for sensitive characteristics like race, to test decision-makers' biases (Ayres and Siegelman 1995). A significant limitation of this method, however, is the inherent impossibility of achieving an exact match between two individuals, precluding perfect comparability (Heckman 1998). Correspondence studies have emerged as a predominant experimental approach for measuring biases (Guryan and Charles 2013, Bertrand and Mullainathan 2004). They create identical fictional profiles with manipulated attributes like race to assess differential treatment. However, these studies traditionally manipulate solely textual information, which may not reflect contemporary decision-making scenarios increasingly influenced by visual cues like facial images, as seen in recent hiring processes (Acquisti and Fong 2020, Ruffle and Shtudiner 2015). This reliance on text limits their effectiveness, as modern contexts often involve multimedia elements, making it challenging to measure real-world biases accurately or correct them based on such incomplete information (Armbruster et al. 2015).

In recent years, remarkable advancements in deep- fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.

The rapid advancement of deepfake technologies, specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across many fields, including forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methods, such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformers, the outcomes achieved in generating deepfakes have been nothing short of astounding and transformative. Also, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Subsequently, the paper is dedicated to accurately benchmarking leading approaches against prominent datasets, offering thorough assessments of the contributions that have significantly impacted these vital domains. Ultimately, we engage in a thoughtful discussion of the existing challenges, paving the way for continuous advancements in this critical and ever-dynamic study area.

An Indian textile baron has revealed that he was duped out of 70 million rupees ( 833,000) by online scammers impersonating federal investigators and even the Supreme Court chief justice. The fraudsters posing as officers from India's Central Bureau of Investigation (CBI) called SP Oswal, chairman and managing director of the textile manufacturer Vardhman, on August 28 and accused him of money laundering. For the next two days, Oswal was under digital surveillance as he was ordered to keep Skype open on his phone 24/7 during which he was interrogated and threatened with arrest. The fraudsters also conducted a fake virtual court hearing with a digital impersonation of Chief Justice of India DY Chandrachud as the judge. Oswal paid the amount after the court verdict via Skype without realising that he was the latest victim of an online scam using a new modus operandi, called "digital arrest".

Scarlett Johansson is speaking out about the reasons she turned down the job of voicing OpenAI's chatbot. Last year, OpenAI CEO Sam Altman reached out to the 39-year-old actress about potentially hiring her to voice the ChatGPT 4.0 system. In an interview with The New York Times, Johansson, who voiced the character of Samantha, an artificial intelligence virtual assistant in the 2013 film "Her," recalled that she said, "No, thank you. Not for me," when Altman approached her about the gig. "I felt I did not want to be at the forefront of that," Johansson told the Times.

British singer-songwriter FKA twigs, born Tahliah Debrett Barnett, testified before the U.S. Senate Judiciary Subcommittee on Intellectual Property on Tuesday about the dangers of artificial intelligence. She relayed that she was especially concerned as an artist whose music and performances are used by third parties to train artificial intelligence models. She said that the power of this technology has become especially apparent to her as she has attempted to build a deepfake version of herself. "In the past year, I have developed my own deepfake version of myself that is not only trained in my personality, but also can use my exact tone of voice to speak many languages," the singer said in her statement. "I will be engaging my'AI twigs' later this year to extend my reach and handle my online social media interactions, whilst I continue to focus on my art from the comfort and solace of my studio."

The pace of the development of Artificial Intelligence (AI) technologies has led to significant concern in many areas of society, including educational contexts. As a result, research agendas on Generative AI (GenAI) in tertiary education have been established (Lodge et al., 2023); however, to date, no review or research agenda has specifically focused on deepfakes in tertiary education. Deepfakes are GenAI outputs which comprise realistic audio, visual, or media outputs that depict false or inaccurate information (Akhtar, 2023). The major consequence of deepfakes is that they can portray an individual doing something or saying something that they have never done, marking an unprecedented shift in the ability to distort reality (Appel & Prietzel, 2022). As tertiary education institutions are centres of learning, the potential implications of such false information are highly important for students, teachers, and university leadership, thus warranting stakeholder attention.