The Role of Artificial Intelligence (AI) in Combatting Deepfakes and Digital Misinformation (original) (raw)
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Expert Systems with Applications, 2024
Due to the fast spread of data through digital media, individuals and societies must assess the reliability of information. Deepfakes are not a novel idea but they are now a widespread phenomenon. The impact of deepfakes and disinformation can range from infuriating individuals to affecting and misleading entire societies and even nations. There are several ways to detect and generate deepfakes online. By conducting a systematic literature analysis, in this study we explore automatic key detection and generation methods, frameworks, algorithms, and tools for identifying deepfakes (audio, images, and videos), and how these approaches can be employed within different situations to counter the spread of deepfakes and the generation of disinformation. Moreover, we explore state-of-the-art frameworks related to deepfakes to understand how emerging machine learning and deep learning approaches affect online disinformation. We also highlight practical challenges and trends in implementing policies to counter deepfakes. Finally, we provide policy recommendations based on analyzing how emerging artificial intelligence (AI) techniques can be employed to detect and generate deepfakes online. This study benefits the community and readers by providing a better understanding of recent developments in deepfake detection and generation frameworks. The study also sheds a light on the potential of AI in relation to deepfakes.
Journal of AI-Assisted Scientific Discovery, 2022
The advent of artificial intelligence (AI) has revolutionized numerous industries, but it has also introduced profound risks, particularly through the development of deepfake technology. Deepfakes, which are AI-generated synthetic media that manipulate visual and audio content to create hyper-realistic but entirely fabricated representations, present a significant threat to data authenticity and public trust. The rapid advancements in machine learning, specifically in generative adversarial networks (GANs), have fueled the proliferation of deepfakes, enabling the creation of indistinguishable digital forgeries that can easily deceive viewers and listeners. This paper explores the multifaceted threat posed by deepfakes in undermining the authenticity of digital content and eroding public confidence in media and information. In an era where visual and auditory content is heavily relied upon for communication, governance, and decision-making, the rise of deepfakes brings forth unprecedented challenges in maintaining the integrity of information. This research examines the technical mechanisms driving deepfake creation, emphasizing the role of GANs and neural networks in producing lifelike simulations of human faces, voices, and behaviors. A detailed analysis is provided on how these technologies can be weaponized for nefarious purposes, such as the dissemination of political misinformation, character defamation, and even identity theft. As the accessibility of AI-driven tools expands, malicious actors are increasingly leveraging deepfakes to manipulate public opinion, disrupt democratic processes, and compromise cybersecurity. The paper highlights the alarming potential of deepfakes to distort reality, making it challenging for individuals and institutions to differentiate between authentic and manipulated content. The paper also delves into the technical countermeasures being developed to detect and mitigate the spread of deepfakes. Current detection methodologies, such as deep learning-based classifiers, digital watermarking, and forensic techniques, are critically evaluated for their effectiveness in identifying manipulated content. However, the ongoing arms race between deepfake creation and detection technologies poses significant challenges, as adversaries continuously refine their models to evade detection systems. This research underscores the need for continued innovation in detection algorithms and the integration of AI-driven solutions to stay ahead of increasingly sophisticated forgeries. Furthermore, the legal and regulatory landscape surrounding deepfakes is scrutinized, with an emphasis on the inadequacies of current frameworks to effectively address the complexities introduced by this technology. The paper discusses potential policy interventions, such as stricter digital content verification laws and international cooperation to combat the proliferation of deepfake-driven misinformation. Legal efforts to hold creators of malicious deepfakes accountable are explored, alongside the ethical considerations involved in balancing free speech with the need for data integrity. Beyond the technical and legal dimensions, this paper also examines the broader societal implications of deepfakes. The erosion of trust in digital media has far-reaching consequences, particularly in the realms of politics, journalism, and corporate governance. Public trust in authoritative sources of information is essential for the functioning of democratic institutions, and deepfakes pose a direct threat to this trust. The paper argues that the widespread dissemination of manipulated content can lead to a destabilization of public discourse, the spread of disinformation, and the breakdown of social cohesion. In addition, the psychological and cultural impacts of deepfakes are explored, highlighting how individuals' perceptions of reality can be shaped and distorted by AI-generated content. The research concludes by offering recommendations for a multi-stakeholder approach to addressing the deepfake phenomenon. This includes fostering collaboration between AI researchers, technologists, policymakers, and civil society organizations to develop comprehensive strategies for mitigating the risks associated with deepfakes. The paper emphasizes the need for a proactive, rather than reactive, approach in dealing with deepfake technology, advocating for the development of robust technical solutions, legal frameworks, and public awareness campaigns to protect the integrity of digital information.
Deepfakes: Threats and Countermeasures Systematic Review
2019
Deepfake, a machine learning-based software tool, has made it easy to alter or manipulate images and videos. Images are frequently used as evidence in investigations and in court. However, technological developments, and deepfake in particular, have potentially made these pieces of evidence unreliable. Altered images and videos are not only surprisingly convincing but are also difficult to identify as fake or real. Deepfakes have been used to blackmail, fake terrorism events, disseminate fake news, defame individuals, and to create political distress. To gain in-depth insight into the deepfake technology, the present research examines its origin and history while assessing how deepfake videos and photos are created. Moreover, the research also focuses on the impact deepfake has made on society in terms of how it has been applied. Different methods have been developed for detecting deepfakes including face detection, multimedia forensics, watermarking, and convolutional neural networ...
Journal of Communication and Management, 2023
Machine learning and artificial intelligence in Journalism are aid and not a replacement or challenge to a journalist's ability. Artificial intelligence-backed fake news characterized by misinformation and disinformation is the new emerging threat in our broken information ecosystem. Deepfakes erode trust in visual evidence, making it increasingly challenging to discern real from fake. Deepfakes are an increasing cause for concern since they can be used to propagate false information, fabricate news, or deceive people. While Artificial intelligence is used to create deepfakes, the same technology is also used to detect them. Digital Media literacy, along with technological deepfake detection tools, is an effective solution to the menace of deepfake. The paper reviews the creation and detection of deepfakes using machine learning and deep learning models. It also discusses the implications of cognitive biases and social identity theories in deepfake creation and strategies for establishing a trustworthy information ecosystem. The researchers have developed a prototype deepfake detection model, which can lay a foundation to expose deepfake videos. The prototype model correctly identified 35 out of 50 deepfake videos, achieving 70% accuracy. The researcher considers 65% and above as "fake" and 65% and below as "real". 15 videos were incorrectly classified as real, potentially due to model limitations and the quality of the deepfakes. These deepfakes were highly convincing and flawless. Deepfakes have a high potential to damage reputations and are often obscene or vulgar. There is no specific law for deepfakes, but general laws require offensive/fake content to be taken down. Deepfakes are often used to spread misinformation or harm someone's reputation. They are designed to harass, intimidate, or spread fear. A significant majority of deepfake videos are pornographic and target female celebrities.
International Journal of Scientific Research and Modern Technology (IJSRMT),Volume 3, Issue 11, 2024
With rapid technological advancements, the emergence of deepfakes and digital misinformation has become both a powerful tool and a formidable challenge. Deepfakes—realistic yet fabricated media generated through artificial intelligence—threaten media credibility, public perception, and democratic integrity. This study explores the intersection of AI technology with these concerns, highlighting AI's role both as a driver of innovation and as a defense mechanism. By conducting an in-depth review of literature, analyzing current technologies, and examining case studies, this research evaluates AI-based strategies for identifying and addressing misinformation. Additionally, it considers the ethical and policy implications, calling for greater transparency, accountability, and media literacy. Through examining present AI techniques and predicting future trends, this paper underscores the importance of collaborative efforts among tech companies, government agencies, and the public to uphold truth and integrity in the digital age.
Deepfakes: A Digital Transformation Leads to Misinformation
2021
Deepfakes are a product of artificial intelligence (AI) and software applications used to create convincing falsified audiovisual content. Linguistically, a portmanteau combines deep learning aspects of AI with the doctored or falsified enhancements that deem the content fake and now deepfake or misinformation results. A variety of sophisticated software programs' exacting algorithms create high-quality videos and manipulated audio of people who may not exist, twisting others who do exist, creating the potential for leading to the spread of serious misinformation often with serious consequences. The rate of detection of this digital emergence is proliferating exponentially and the sourcing is challenging to verify, causing alarms. Examples of this pervasive information warfare are is associated with deepfakes that range from identity theft, discrediting public figures and celebrities, cyberbullying, blackmail, threats to national security, personal privacy, intensifying pornography and sexual exploitation, cybersecurity, baiting hate crimes, abusing social media platforms and manipulating metadata. Deepfakes that are difficult to cite, acquire, or track have some parallel attributes to grey literature by that definition. Often detectable, yet problematic, activities such as phishing and robocalling may be common attempts of deepfake activities that threaten and interrupt rhythms of daily life. The increasing online personas that many people create or assume contribute to this fake content and potential for escalated exploitation due to technical abilities to copy and reimagine details that are not true. AI image generators create completely false images of people that simply don't exist within seconds and are nearly impossible to track. While AI is perceived as a positive benefit for science and policy, it can have negative roles in this new AI threatened environment. Deepfakes have crossover targets in common business applications and society at large. Examples of this blur are targeted advertising, undetected security cameras in public spaces, blockchain, tabloid press/paparazzi, entertainment, computer games, online publishing, data and privacy, courtroom testimony, public opinion, scientific evidence, political campaigns, and rhetoric. This paper explores the impact and intersections of these behaviors and activities, products of AI, and emerging technologies with how digital grey and the optics of grey expose the dangers of deepfakes on everyday life. Applying a security and privacy lens, we offer insights of extending libel and slander into more serious criminal behavior as deepfakes become more pervasive, construing reality, endangering personal, social, and global safety nets adding to the new normal we assume today. How we became more sensitized to misinformation and fake news tells the story about deepfakes.
A Survey of Different Methods used in Detecting Deepfakes
Pramana Research, 2021
Deepfake is a hybrid of the fake and deep-learning technologies. Deep learning is an artificial intelligence function that can be used to both build and identify deepfakes. Fake films, photos, news, and terrorist incidents are all created using Deepfake algorithms.When the number of deepfake videos and photos on social media rises, people will lose faith in the truth.Artificial intelligence breakthroughs have made it increasingly difficult to distinguish between real and counterfeit information, particularly photos and videos. Deepfake films, which are created by modifying videos using advanced machine learning techniques, are a recent invention. In the destination video, the face of an individual from the source video is replaced with the face of a second person. As deepfakes get more seamless and easier to compute, this concept is becoming further polished.Deepfakes, when combined with the scope and speed of social media, might easily deceive people by portraying someone saying things that never happened, leading to people believing imaginary scenarios, causing distress, and propagating fake news. Individuals, communities, organisations, security, religions, and democracy are all being impacted by deepfakes. In this study, we look at a number of strategies that can be used to identify deepfake videos. We employ a Transfer Learning strategy in which the system applies the feature information it learned while training on the ImageNet dataset and updates itself while training on our dataset.The trained models are used to classify counterfeit and unaltered videos. We then perform a comparative analysis on their performance metrics.
Deepfakes: Current and Future Trends
Zenodo (CERN European Organization for Nuclear Research), 2022
Advances in Deep Learning (DL), Big Data and image processing have facilitated online disinformation spreading through Deepfakes. This entails severe threats including public opinion manipulation, geopolitical tensions, chaos in financial markets, scams, defamation and identity theft among others. Therefore, it is imperative to develop techniques to prevent, detect, and stop the spreading of deepfake content. Along these lines, the goal of this paper is to present a big picture perspective of the deepfake paradigm, by reviewing current and future trends. First, a compact summary of DL techniques used for deepfakes is presented. Then, a review of the fight between generation and detection techniques is elaborated. Moreover, we delve into the potential that new technologies, such as distributed ledgers and blockchain, can offer with regard to cybersecurity and the fight against digital deception. Two scenarios of application, including online social networks engineering attacks and Internet of Things, are reviewed where main insights and open challenges are tackled. Finally, future trends and research lines are discussed, pointing out potential key agents and technologies.