Fake News Detection Ai | AI Final Year Project Report

Complete Technical Paper Content — Fake News Detection Ai

1. Executive Abstract, Problem Formulation & Background

Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact. While the act of creating fake content is not new, deepfakes uniquely leverage machine learning and artificial intelligence techniques, including facial recognition algorithms and artificial neural networks such as variational autoencoders and generative adversarial networks (GANs). In turn, the field of image forensics has worked to develop 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. Academics have raised concerns about the potential for deepfakes to promote disinformation and hate speech, as well as interfere with elections. In response, the information technology industry and governments have proposed recommendations and methods to detect and mitigate their use. Academic research has also delved deeper into the factors driving deepfake engagement online as well as potential countermeasures to malicious application of deepfakes. From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing for the disruption of the entertainment and media industries.

Generalization. High-quality deepfakes are often achieved by training on hours of footage of the target. This challenge is to minimize the amount of training data and the time to train the model required to produce quality images and to enable the execution of trained models on new identities (unseen during training). Paired Training. Training a supervised model can produce high-quality results, but requires data pairing. This is the process of finding examples of inputs and their desired outputs for the model to learn from. Data pairing is laborious and impractical when training on multiple identities and facial behaviors. Some solutions include self-supervised training (using frames from the same video), the use of unpaired networks such as Cycle-GAN, or the manipulation of network embeddings. Identity leakage. This is where the identity of the driver (i.e., the actor controlling the face in a reenactment) is partially transferred to the generated face. Some solutions proposed include attention mechanisms, few-shot learning, disentanglement, boundary conversions, and skip connections. Occlusions. When part of the face is obstructed with a hand, hair, glasses, or any other item then artifacts can occur. A common occlusion is a closed mouth which hides the inside of the mouth and the teeth. Some solutions include image segmentation during training and in-painting. Temporal coherence. In videos containing deepfakes, artifacts such as flickering and jitter can occur because the network has no context of the preceding frames. Some researchers provide this context or use novel temporal coherence losses to help improve realism. As the technology improves, the interference is diminishing. Overall, deepfakes are expected to have several implications in media and society, media production, media representations, media audiences, gender, law, and regulation, and politics.

On 8 June 2022, Daniel Emmet, a former AGT contestant, teamed up with the AI startup Metaphysic AI, to create a hyperrealistic deepfake to make it appear as Simon Cowell. Cowell, notoriously known for severely critiquing contestants, was on stage interpreting "You're The Inspiration" by Chicago. Emmet sang on stage as an image of Simon Cowell emerged on the screen behind him in flawless synchronicity. On 30 August 2022, Metaphysic AI had 'deep-fake' Simon Cowell, Howie Mandel and Terry Crews singing opera on stage. On 13 September 2022, Metaphysic AI performed with a synthetic version of Elvis Presley for the finals of America's Got Talent. The MIT artificial intelligence project 15.ai has been used for content creation for multiple Internet fandoms, particularly on social media. In 2023 the bands ABBA and Kiss partnered with Industrial Light & Magic and Pophouse Entertainment to develop deepfake avatars capable of performing virtual concerts.

In February 2018, in separate videos, the face of the Argentine President Mauricio Macri had been replaced by the face of Adolf Hitler, and Angela Merkel's face has been replaced with Donald Trump's. In April 2018, Jordan Peele collaborated with BuzzFeed to create a deepfake of Barack Obama with Peele's voice; it served as a public service announcement to increase awareness of deepfakes. In January 2019, Fox affiliate KCPQ aired a deepfake of Trump during his Oval Office address, mocking his appearance and skin colour. The employee found responsible for the video was subsequently fired. In June 2019, the United States House Intelligence Committee held hearings on the potential malicious use of deepfakes to sway elections. In April 2020, the Belgian branch of Extinction Rebellion published a deepfake video of Belgian Prime Minister Sophie Wilmès on Facebook. The video promoted a possible link between deforestation and COVID-19. It had more than 100,000 views within 24 hours and received many comments. On the Facebook page where the video appeared, many users interpreted the deepfake video as genuine. During the 2020 US presidential campaign, many deepfakes surfaced purporting Joe Biden in cognitive decline—falling asleep during an interview, getting lost, and misspeaking—all bolstering rumours of his decline. During the 2020 Delhi Legislative Assembly election campaign, the Delhi Bharatiya Janata Party used similar technology to distribute a version of an English-language campaign advertisement by its leader, Manoj Tiwari, translated into Haryanvi to target Haryana voters. A voiceover was provided by an actor, and AI trained using video of Tiwari speeches was used to lip-sync the video to the new voiceover. A party staff member described it as a "positive" use of deepfake technology, which allowed them to "convincingly approach the target audience even if the candidate didn't speak the language of the voter." In 2020, Bruno Sartori produced deepfakes parodying politicians like Jair Bolsonaro and Donald Trump. In April 2021, politicians in a number of European countries were approached by pranksters Vovan and Lexus, who are accused by critics of working for the Russian state. They impersonated Leonid Volkov, a Russian opposition politician and chief of staff of the Russian opposition leader Alexei Navalny's campaign, allegedly through deepfake technology. However, the pair told The Verge that they did not use deepfakes, and just used a look-alike. In May 2023, a deepfake video of Vice President Kamala Harris supposedly slurring her words and speaking nonsensically about today, tomorrow and yesterday went viral on social media. In June 2023, in the United States, Ron DeSantis's presidential campaign used a deepfake to misrepresent Donald Trump. In November 2023, a deepfake video of the German Chancellor Olaf Scholz announcing a plan to ban the political activities of the AfD was uploaded to YouTube by the Zentrum für Politische Schönheit (Center of Political Beauty). In March 2024, during India's state assembly elections, deepfake technology was widely employed by political candidates to reach out to voters. Many politicians used AI-generated deepfakes created by startup The Indian Deepfaker, founded by Divyendra Singh Jadoun, to translate their speeches into multiple regional languages, allowing them to engage with diverse linguistic communities across the country. This surge in the use of deepfakes for political campaigns marked a significant shift in electioneering tactics in India. In June 2025, Javier Milei's government backed a smear campaign against journalist Mengolini, which was partly based on explicit deepfakes. In July 2025, Donald Trump posted a deepfake on his Truth Social account, depicting former president Barack Obama getting arrested at the White House and put in prison.

2. Related Work, Literature Survey & Algorithmic Foundations

In 2017, Deepfake pornography prominently surfaced on the Internet, particularly on Reddit. As of 2019, many deepfakes on the internet feature pornography of female celebrities whose likeness is typically used without their consent. A report published in October 2019 by Dutch cybersecurity startup Deeptrace estimated that 96% of all deepfakes online were pornographic. As of 2018, a Daisy Ridley deepfake first captured attention, among others. As of October 2019, most of the deepfake subjects on the internet were British and American actors. However, around a quarter of the subjects are South Korean, the majority of which are K-pop stars. In June 2019, a downloadable Windows and Linux application called DeepNude was released that used neural networks, specifically generative adversarial networks, to remove clothing from images of women. The app had both a paid and unpaid version, the paid version costing $50. On 27 June the creators removed the application and refunded consumers. Female celebrities are often a main target when it comes to deepfake pornography. In 2023, deepfake porn videos appeared online of Emma Watson and Scarlett Johansson in a face swapping app. In 2024, deepfake porn images circulated online of Taylor Swift. Academic studies have reported that women, LGBT people and people of colour (particularly activists, politicians and those questioning power) are at higher risk of being targets of promulgation of deepfake pornography. Deepfake technology has become a tool for gender-based harassment and violence, proportionally targeting women and marginalized groups. There is an increasing ethical and equity concerns of intimidation and reputational harm intentions behind this specific media.

The liar's dividend is a political and social phenomenon in which, when faced with incriminating or embarrassing authentic video or audio recordings of themselves, individuals will claim that the recordings are AI in order to dismiss the claims and gain sympathy.

Barack Obama On 17 April 2018, American actor Jordan Peele, BuzzFeed, and Monkeypaw Productions posted a deepfake of Barack Obama to YouTube, which depicted Barack Obama cursing and calling Donald Trump names. In this deepfake, Peele's voice and face were transformed and manipulated into those of Obama. The intent of this video was to portray the dangerous consequences and power of deepfakes, and how deepfakes can make anyone say anything. Donald Trump On 5 May 2019, Derpfakes posted a deepfake of Donald Trump to YouTube, based on a skit Jimmy Fallon performed on The Tonight Show. In the original skit (aired 4 May 2016), Jimmy Fallon dressed as Donald Trump and pretended to participate in a phone call with Barack Obama, conversing in a manner that presented him to be bragging about his primary win in Indiana. In the deepfake, Jimmy Fallon's face was transformed into Donald Trump's face, with the audio remaining the same. This deepfake video was produced by Derpfakes with a comedic intent. In March 2023, a series of images appeared to show New York Police Department officers restraining Trump. The images, created using Midjourney, were initially posted on Twitter by Eliot Higgins but were later re-shared without context, leading some viewers to believe they were real photographs. Nancy Pelosi In 2019, a clip from Nancy Pelosi's speech at the Center for American Progress (given on 22 May 2019) in which the video was slowed down, in addition to the pitch of the audio being altered, to make it seem as if she were drunk, was widely distributed on social media. Critics argue that this was not a deepfake, but a shallowfake‍—‍a less sophisticated form of video manipulation. Mark Zuckerberg In May 2019, two artists collaborating with the company CannyAI created a deepfake video of Facebook founder Mark Zuckerberg talking about harvesting and controlling data from billions of people. The video was part of an exhibit to educate the public about the dangers of artificial intelligence. Kim Jong-un and Vladimir Putin On 29 September 2020, deepfakes of North Korean leader Kim Jong-un and Russian President Vladimir Putin were uploaded to YouTube, created by a nonpartisan advocacy group RepresentUs. The deepfakes of Kim and Putin were meant to air publicly as commercials to relay the notion that interference by these leaders in US elections would be detrimental to the United States' democracy. The commercials also aimed to shock Americans to realize how fragile democracy is, and how media and news can significantly influence the country's path regardless of credibility. However, while the commercials included an ending comment detailing that the footage was not real, they ultimately did not air due to fears and sensitivity regarding how Americans may react. On 5 June 2023, an unknown source broadcast a reported deepfake of Vladimir Putin on multiple radio and television networks. In the clip, Putin appears to deliver a speech announcing the invasion of Russia and calling for a general mobilization of the army. Volodymyr Zelenskyy On 16 March 2022, a one-minute long deepfake video depicting Ukraine's president Volodymyr Zelenskyy seemingly telling his soldiers to lay down their arms and surrender during the 2022 Russian invasion of Ukraine was circulated on social media. Russian social media boosted it, but after it was debunked, Facebook and YouTube removed it. Twitter allowed the video in tweets where it was exposed as a fake, but said it would be taken down if posted to deceive people. Hackers inserted the disinformation into a live scrolling-text news crawl on TV station Ukraine 24, and the video appeared briefly on the station's website in addition to false claims that Zelenskyy had fled his country's capital, Kyiv. It was not immediately clear who created the deepfake, to which Zelenskyy responded with his own video, saying, "We don't plan to lay down any arms. Until our victory." Wolf News In late 2022, pro-China propagandists started spreading deepfake videos purporting to be from "Wolf News" that used synthetic actors. The technology was developed by a London company called Synthesia, which markets it as a cheap alternative to live actors for training and HR videos. Pope Francis In March 2023, an anonymous construction worker from Chicago used Midjourney to create a fake image of Pope Francis in a white Balenciaga puffer jacket. The image went viral, receiving over twenty million views. Writer Ryan Broderick dubbed it "the first real mass-level AI misinformation case". Experts consulted by Slate characterized the image as unsophisticated: "you could have made it on Photoshop five years ago". Keir Starmer In October 2023, a deepfake audio clip of the UK Labour Party leader Keir Starmer abusing staffers was released on the first day of a Labour Party conference. The clip purported to be an audio tape of Starmer abusing his staffers. Rashmika Mandanna In early November 2023, a famous South Indian actor, Rashmika Mandanna fell prey to DeepFake when a morphed video of a famous British-Indian influencer, Zara Patel, with Rashmika's face started to float on social media. Zara Patel claims to not be involved in its creation. Bongbong Marcos In April 2024, a deepfake video misrepresenting Philippine President Bongbong Marcos was released. It is a slideshow accompanied by a deepfake audio of Marcos purportedly ordering the Armed Forces of the Philippines and special task force to act "however appropriate" should China attack the Philippines. The video was released amidst tensions related to the South China Sea dispute. The Presidential Communications Office has said that there is no such directive from the president and said a foreign actor might be behind the fabricated media. Criminal charges has been filed by the Kapisanan ng mga Brodkaster ng Pilipinas in relation to the deepfake media. On 22 July 2024, a video of Marcos purportedly snorting illegal drugs was released by Claire Contreras, a former supporter of Marcos. Dubbed as the polvoron video, the media noted its consistency with the insinuation of Marcos' predecessor—Rodrigo Duterte—that Marcos is a drug addict; the video was also shown at a Hakbang ng Maisug rally organized by people aligned with Duterte. Two days later, the Philippine National Police and the National Bureau of Investigation, based on their own findings, concluded that the video was created using AI; they further pointed out inconsistencies with the person on the video with Marcos, such as details on the two people's ears. Joe Biden Prior to the 2024 United States presidential election, phone calls imitating the voice of the incumbent Joe Biden were made to dissuade people from voting for him. The person responsible for the calls was charged with voter suppression and impersonating a candidate. The FCC proposed to fine him US$6 million and Lingo Telecom, the company that allegedly relayed the calls, $2 million. Arup Group The firm Arup Group lost $25 million in 2024 from a deepfake scam. Sara Duterte Philippine Senator Ronald dela Rosa on 14 June 2025 shared a 40-second Deepfake video created via Veo where two students on the street testified on why the impeachment proceedings against Vice President Sara Duterte is selective and politically motivated. Dela Rosa lauded the supposed youth for their opinions. When informed it was an AI video, he says that if that was the case the video creator had a point. Duterte herself defended Dela Rosa, insisting that sharing an AI video is not wrong as long as its "not for profit". Catherine Connolly In October 2025, Irish presidential candidate Catherine Connolly condemned an AI-generated deepfake video that mimicked an RTÉ News report and falsely claimed she was withdrawing from the presidential election; her campaign reported the video to the social media platforms it appeared on.

Sasse, Ben (19 October 2018). "This New Technology Could Send American Politics into a Tailspin". Opinions. The Washington Post. Retrieved 10 July 2019. Fake/Spoof Audio Detection Challenge (ASVspoof) Deepfake Detection Challenge (DFDC) Bibliography: Media Literacy in the Age of Deepfakes. Curated by Dr Joshua Glick. Before you believe – how to recognize a deepfake and is it inherently evil? Tyagi, Vipin (2026). Digital Image Forgery Detection: Techniques, Challenges and Applications. SpringerBriefs in Computer Science. Singapore: Springer Nature Singapore. doi:10.1007/978-981-95-3004-5. ISBN 978-981-953003-8. OCLC 1547114493.

3. Dataset Ingestion, Preprocessing & Stochastic Augmentations

Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, computer code or other forms of digital data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts. The prevalence of generative AI tools has increased significantly since the AI boom of the early 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include chatbots such as ChatGPT, Claude, Microsoft Copilot, DeepSeek, Doubao, Google Gemini, Grok, Kimi and Qwen; text-to-image models such as DALL-E, Adobe Firefly, Stable Diffusion, and Midjourney; text-to-speech models such as 15.ai and ElevenLabs; text-to-music models such as Suno and Udio; translation models such as Google Translate and DeepL; and text-to-video models such as Veo, LTX and Sora. Companies in a variety of sectors have used or proposed the use of generative AI, including those in software development, healthcare, finance, entertainment, sales and marketing, screenwriting, and product design. Generative AI models can produce incorrect answers or "hallucinations." Generative AI has been used for cybercrime, and to deceive and manipulate people through fake news and deepfakes. Generative AI models have been trained on copyrighted works without the rightholders' permission. Many generative AI systems are reliant on large-scale data centers, whose environmental impacts include electronic waste, consumption of fresh water for cooling, and high energy consumption that is estimated to be growing steadily.

Machine learning uses both discriminative models and generative models to predict or generate data. Beginning in the late 2000s and early 2010s, advances in deep learning led to major improvements in image classification, speech recognition, and natural language processing. Neural networks in this period were typically trained as discriminative models due to the relative difficulty of training generative models. In 2014, the introduction of models such as the variational autoencoder (VAE) and generative adversarial network (GAN) enabled effective deep generative modeling of complex data such as images. In 2017, the paper "Attention Is All You Need" proposed that transformer architecture could enable further advances in generative modeling compared to earlier long short-term memory (LSTM) networks. This led to the development of generative pre-trained transformer (GPT) models, beginning with GPT-1 in 2018.

In March 2020, the release of 15.ai, a free web application created by an anonymous MIT researcher that could generate convincing character voices using minimal training data, was one of the earliest publicly available uses for generative AI. The platform is credited as the first mainstream service for audio deepfakes. In 2021, DALL-E, a closed-source transformer-based generative model developed by OpenAI, drew widespread attention to text-to-image generation. Other projects, including open-source approaches such as VQGAN+CLIP and DALL·E Mini (later renamed Craiyon), made similar systems more accessible to the public. Dream by Wombo was released at the end of 2021, followed by the releases of Midjourney and Stable Diffusion in 2022. In November 2022, ChatGPT was released to the public. By 2023, it popularized generative AI for general-purpose text-based tasks.

In a 2024 survey by marketing research firm Ipsos, Asia–Pacific countries were significantly more optimistic than Western societies about generative AI and show higher adoption rates. Despite expressing concerns about privacy and the pace of change, 68% of Asia-Pacific respondents believed that AI was having a positive impact on the world, compared to 57% globally. According to a survey by SAS and Coleman Parkes Research, as of 2023, 83% of Chinese respondents were using the technology, exceeding both the global average of 54% and the U.S. rate of 65%. A UN report indicated that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, more than any other country. A 2024 survey by the Just So Soul social media app reported that 18% of respondents born after 2000 used generative AI "almost every day", and that over 60% of respondents like or love AI-generated content (AIGC), while less than 3% dislike or hate it. By mid-2025, companies were increasingly abandoning generative AI pilot projects as they had difficulties with integration, data quality and unmet returns, leading analysts at Gartner and The Economist to characterize the period as entering the Gartner hype cycle's "trough of disillusionment" phase. In an October 2025 survey by the Federal Reserve, one in four U.S. workers reported using generative AI at work during the previous month.

4. Neural Network Backbone, Layer Specifications & Loss Formulations

Generative artificial intelligence has been applied across multiple industries for content creation and automation. In healthcare, generative models are used for drug discovery and the generation of synthetic medical data to train diagnostic systems. In finance, they are used for report drafting, data generation, and customer service automation. Media and entertainment industries use generative systems for tasks such as music composition, script development, and image or video generation. Researchers and policymakers have raised concerns regarding accuracy, misuse, and impacts on academic and professional work.

Large language models (LLMs) are trained on tokenized text from large corpora and are capable of natural language processing, machine translation, and natural language generation. LLMs can be used as foundation models for a variety of downstream tasks. They can also be trained on source code to generate programs from prompts. Many applications combine large language models with external knowledge sources using retrieval-augmented generation (RAG), a technique in which relevant documents are retrieved at inference time and incorporated into the model's response. Generative AI has been integrated into content management and website publishing platforms, where large language models are used to generate text, assist with page composition, and automate website creation from natural language prompts. WordPress.com is one example of this application, incorporating generative AI into its website authoring workflow.

In 2016, DeepMind's WaveNet demonstrated that deep neural networks can generate raw audio waveforms. This enabled more realistic speech synthesis compared to earlier approaches. Subsequent systems such as Tacotron 2 demonstrated end-to-end neural text-to-speech generation.

Generative AI can be used to create visual art. Such systems are trained on image–text pairs. Examples include Stable Diffusion, DALL-E, and Midjourney. Generative AI has also been applied to low-bitrate image compression, enabling the reconstruction of perceptually realistic images from compressed data. Recent approaches have explored controllable compression at multiple bitrates using a single model.

5. Training Protocol, Convergence Behavior & Experimental Evaluation

Generative AI can be used to produce photorealistic videos. Systems such as Runway have demonstrated text-to-video generation capabilities.

Generative AI systems have been used in mathematics and computer science to generate candidate computer programs, proofs, constructions, or algorithms. In 2023, Google DeepMind introduced FunSearch, a method for creating computer programs that solve mathematical and algorithmic problems. FunSearch was used to discover new mathematical constructions in the cap set problem and the bin packing problem. In 2023, Google DeepMind introduced AlphaDev, which was used to discover small sorting algorithms that outperformed previously known human benchmarks and have been integrated into the LLVM standard C++ sorting library. In 2025, Google DeepMind introduced AlphaEvolve, an AI system for general-purpose algorithm discovery and optimization. AlphaEvolve uses LLMs to propose code changes, automated evaluators to assess each candidate, and an evolutionary process to iteratively improve algorithms. In 2026, in response to the increasing use of generative AI in mathematical discovery, a group of mathematicians issued the Leiden Declaration on Artificial Intelligence and Mathematics, which recommends disclosing the use of AI in research papers, ensuring that AI-assisted papers are peer-reviewed, and providing legal resources and public funding so that academia and for-profit companies can compete on equal terms.

In 2023, Google DeepMind introduced GNoME, a method to propose candidate inorganic crystal structures for computational screening and experimental synthesis in material science. Other material science methods include MatterGen, CDVAE, and CrystalFlow.

Generative engine optimization (GEO) is the practice of structuring digital content and managing online presence to improve visibility in responses generated by generative AI systems. The practice influences the way large language models (LLMs) retrieve, summarize, and present information in response to user queries. Related terms include answer engine optimization (AEO) and artificial intelligence optimization (AIO).