Deepfake Research Articles

A Multidisciplinary Threats to Emerging Cybersecurity, Legal and Ethical Threats Posed by Deepfake Technology

The creation of remarkably realistic but phony audio and video content through the use of deepfake technology raises a number of issues with cybersecurity, law and ethics. Deep fakes undermine the credibility of online information sources by posing risks including identity theft and the dissemination of false information. Legally speaking, the rise of deepfakes highlights regulatory inadequacies and enforcement concerns while posing difficult considerations about privacy rights and responsibility. Due to the potential for reputational injury and the perpetuation of negative stereotypes, deep fakes raise ethical questions about permission, privacy and harm to society. Deep fakes have become more common as a result of artificial intelligence's quick development which calls for preventative steps to counter new dangers. To effectively secure against misuse and maintain the integrity of digital material, this research paper proposes for a multidisciplinary strategy that takes legal, ethical and cybersecurity concerns into account

Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks

As facial modification technology advances rapidly, it poses a challenge to methods used to detect fake faces. The advent of deep learning and AI-based technologies has led to the creation of counterfeit photographs that are more difficult to discern apart from real ones. Existing Deep fake detection systems excel at spotting fake content with low visual quality and are easily recognized by visual artifacts. The study employed a unique active forensic strategy Compact Ensemble-based discriminators architecture using Deep Conditional Generative Adversarial Networks (CED-DCGAN), for identifying real-time deep fakes in video conferencing. DCGAN focuses on video-deep fake detection on features since technologies for creating convincing fakes are improving rapidly. As a first step towards recognizing DCGAN-generated images, split real-time video images into frames containing essential elements and then use that bandwidth to train an ensemble-based discriminator as a classifier. Spectra anomalies are produced by up-sampling processes, standard procedures in GAN systems for making large amounts of fake data films. The Compact Ensemble discriminator (CED) concentrates on the most distinguishing feature between the natural and synthetic images, giving the generators a robust training signal. As empirical results on publicly available datasets show, the suggested algorithms outperform state-of-the-art methods and the proposed CED-DCGAN technique successfully detects high-fidelity deep fakes in video conferencing and generalizes well when comparing with other techniques. Python tool is used for implementing this proposed study and the accuracy obtained for proposed work is 98.23 %.

Strengthening India’s Cybersecurity and Data Privacy Landscape: A Comprehensive Overview

This article chronicles India’s cybersecurity and data privacy journey, from the inception of the Information Technology Act in 2000 to the latest Digital Personal Data Protection Act of 2023. Examining key legislative developments, committee formations and judicial interventions, it highlights India’s commitment to fortifying its cybersecurity framework. The topic is analysed in a substantially analytical and qualitative manner, primarily based on literature, both primary and secondary. It is descriptive and puts forth a case explanation of cyber security in banking. In the face of escalating challenges in the digital era, the article contributes to the discourse by addressing prevalent threats like deep fakes, existing frameworks and innovative strategies for securing digital assets. Emphasising the critical interplay of technology, governance and the legal framework, the article underscores the imperative of robust cybersecurity measures to safeguard citizens’ and government interests.

A Systematic Review on Deep Fake Image Generation, Detection Techniques, Ethical Implications, and Overcoming Challenges

Deepfake technology, rooted in sophisticated machine learning techniques, utilizes deep neural networks to create highly realistic fake content such as videos, audio recordings, and images. This technology has rapidly evolved due to advancements in deep learning models, computational power, and data availability. The ethical implications, social impact, misuse, and legal frameworks surrounding Deepfake technology have been extensively studied. Detection techniques using deep learning approaches have been developed to combat the challenges posed by Deepfake content. Recommendations for future research include enhancing detection techniques, integrating explainable AI, and exploring real-time detection systems. Industry and policy implications emphasize the need for robust detection technologies, comprehensive legal frameworks, and collaborative efforts to address ethical concerns and regulate Deepfake content. This systematic review explores the landscape of deep fake image generation, detection techniques and challenges, in addition to ethical considerations. By synthesizing existing research, we aim to provide insights into deep-fake technology's advancements, limitations, and societal implications. This review underscores the urgent need for interdisciplinary collaboration and robust frameworks to address the multifaceted issues surrounding deep fakes in the digital age.

Artificial Intelligence and Its Challenges To Elections In Indonesia: A Legal Analysis

The improper utilization of AI technology poses difficulties to democracy, particularly the growing threat of unjust elections, exemplified by the deployment of bot accounts and deep fakes during electoral processes. Hence, it is crucial to build a strong and comprehensive framework to regulate the utilization of AI technology in Indonesia's political process. This article analyzes four main topics: (a) the advancement of AI technology and its connection to elections; (b) the influence of AI technology on election principles; (c) the pressing need for regulating AI in elections; and (d) the possibilities and difficulties of regulating AI technology within Indonesia's legal framework. The paper employs doctrinal legal research to examine the necessity of regulating AI technology in the context of conducting elections, taking into account the constitutional framework, established principles, and democratic election norms. The result shows that irresponsible use of AI technology remains a menace to democratic election ideals, and Indonesia must establish adequate legal mechanisms to tackle the problems stemming from the improper use of AI technology in the political process. The regulation of AI technology can be initiated by introducing a bill specifically focused on artificial intelligence (AI). This process should also involve the synchronization and harmonization of election rules, including election laws, laws governing the election of governors, regents, and mayors, laws concerning political parties, and other implementing regulations such as those established by the General Election Commission and the Election Supervisory Board.

Exploring Legal and Technical Challenges of Deep Fakes in India

The rapid advancement of deep fake technology has introduced significant challenges in the realms of data security, privacy, and intellectual property rights. Deep fakes, which involve the creation of highly realistic but manipulated digital content using artificial intelligence, have been misused for various malicious purposes, including political manipulation, identity theft, and defamation. This article examines the current state of deep fake regulation in India, highlighting the inadequacies of existing laws and the urgent need for specific legislation to address these issues. Despite the presence of broader laws under the Information Technology Act and the Indian Penal Code, there is a conspicuous absence of precise legal frameworks targeting the unique threats posed by deep fakes. Through an interdisciplinary approach that integrates legal analysis and technical insights, this study evaluates the effectiveness of current detection technologies and explores their limitations. The article also provides case studies illustrating the misuse of deep fakes in political and personal contexts, underscoring the real-world impact of this technology. In conclusion, the study advocates for comprehensive legal reforms and enhanced technological measures to mitigate the risks associated with deep fakes. By proposing targeted amendments to existing laws and recommending the development of advanced detection tools, this research aims to contribute to a more robust and effective regulatory environment for deep fakes in India.

Deep fakes and the Artificial Intelligence Act—An important signal or a missed opportunity?

AbstractThe Artificial Intelligence Act (AI Act) adopted by the European Union might serve as a global regulatory reference point. Heated negotiations over the AI Act have shown that reconciling the interests of numerous stakeholders is not an easy task. As well as creating clear and precise rules that would enable implementing effective safeguards for citizens against the manipulative potential of technology. The AI Act introduces the first legal definition of deep fakes and creates a system of protection against their harmful applications, that is based on transparency obligations. I argue that in the course of negotiations, the EU has made progress in regulating deep fakes, but the adopted solutions should only be an introduction to a more strict protection of individuals. The AI Act targets a specific group of deep fakes, leaving some of them unregulated or poorly regulated and disregarding the consequences of nonconsensual deep fake pornography. By analyzing the legal provisions related to deep fakes in the AI Act, I point to the rationale behind the chosen forms of protection, criticize the shortcomings and list the consequences of adopting the AI Act for deep fakes landscape.

Challenges faced by Nigerian fact-checkers with deep fakes during the 2023 general election

Challenges faced by Nigerian fact-checkers with deep fakes during the 2023 general election

Feature fusion Vision Transformers using MLP-Mixer for enhanced deepfake detection

Deepfake technology, utilizing deep learning and computer vision, presents significant security threats by generating highly realistic synthetic media, such as images and videos. In this paper, we propose a feature fusion-based method for deepfake detection, harnessing the capabilities of three Vision Transformer (ViT) architectures: Dual Attention Vision Transformers (DaViT), Inception Transformer (iFormer), and Group Propagation Vision Transformer (GPViT). The proposed feature fusion method integrates the local–global contextual understanding of DaViT, the frequency spectrum broadening of iFormer, and the high-resolution feature retention of GPViT to provide a comprehensive analysis of visual data, crucial for detecting subtle manipulations typical in deepfakes. An MLP-Mixer (Multi-Layer Perceptron-Mixer) model is proposed for the feature fusion process for effective feature integration, enhancing the detection capabilities while preserving the individual strengths of each ViT model. The performance of the proposed method is evaluated using standard datasets, namely FaceForensics++ and Celeb-DF, demonstrating its robustness and generalizability across various scenarios. For the four manipulation techniques in the FaceForensics++ dataset, namely DeepFakes (DF), Face2Face (F2F), FaceSwap (FS), and NeuralTextures (NT), the proposed method achieves area under the curve (AUC) of 99.75%, 97.04%, 98.78%, and 87.49% respectively, Moreover, it demonstrates an AUC of 99.84% on the Celeb-DF dataset. The performance of the proposed model has been evaluated against many state-of-the-art methods to ensure its robustness and generalization capabilities.

Exploring Cyber Fraud within the South African Cybersecurity Legal Framework

All countries are globally struggling with the challenges cybercrime presents to the cybersecurity legal framework. Fraud is not a new crime and existed long before the internet. The internet provides a threat actor access to a lot of potential victims and the use of various threat vectors to gain access to personal information by means of social engineering. It is therefore not surprising that cyber fraud has become a serious threat which continues to escalate globally. In 2021, around $100 million was lost in Canada due to online fraud. The United Kingdom (UK) Finance indicated that cyber fraud costs consumers more than £1.2 billion in 2022. The South African (SA) Fraud Prevention Services noted a 356% surge in identity fraud between April 2022 and April 2023. The cybersecurity threat landscape is ever-evolving with the UK Finance warning that the number of cyber frauds could surge out of control as threat actors begin to incorporate the use of Artificial intelligence (AI) to make their operations far more sophisticated and not as easily detected. In 2023 the United States (US) also warned that the irresponsible use of AI could exacerbate societal harms such as fraud. Cyber fraud, also referred to as a “white collar” or commercial crime, is an umbrella term to describe the commission of different types of cyber fraud by means of the use of various threat vectors. The threat vector used to commit the different type of fraud is continuously evolving, such as the use of sophisticated phishing to quishing and deep fakes which are aimed at deceiving the recipient in sharing information. The information obtained from a data breach may be used to commit cyber fraud. Irrespective of the threat vector used to commit fraud, all types of fraud present with the same elements, namely a threat actor who unlawfully and intentionally deceives a victim to benefit and cause harm. The discussion focuses on cyber fraud in general and not a specific type of cyber fraud. The purpose of the discussion is to provide an overview of the challenges cyber fraud present to the South African cybersecurity legal landscape.

DL MODEL FOR IMITATION FALSE FACE, AGE, GENDER AND SCENARIO DISPARITIES

Scrutinises many methods that are employed by numerous scholars to identify Deep phoney films. We offer a method for spotting identity changes, such face swapping, in a single photograph. Deep Fake and other facial swapping methods alter the face region in an effort to replicate the appearance of the context while changing only the face. We show that the two regions differ as a result of this mode of operation. These variations raise red flags for manipulative activity. Our method makes use of a pair of networks: one for face identification, that takes into account face region included within accurate semantic segmentation & specifies gender and age, and another for context recognition, which takes into account face's surrounding context. Recognizing the Meaning of Emoticons, we provide a strategy which makes use of recognition signals from our 2 networks to pinpoint these deviations, so offering an enhanced, non-traditional detection signal. We propose a deep fake-detection method based on an organ-level transformer model for extracting deep fake characteristics. By giving less weight to identification of deformed or unclear organs, we give priority to detection of obvious and intact organs. Our system can also detect subtle alterations to facial expressions and details, as well as heavily tainted, digitally-generated phony images. So-called "micro expressions" are fast, fleeting facial changes. This kind of unrestrained display of emotion is a window into a person's true feelings. examined the seven most common micro expressions seen in human faces: happiness, sadness, anger, disgust, contempt, fear, & surprise.

Deepfake Detection System

Abstract: Because deep fake technology allows for the creation of incredibly realistically modified material that has the potential to mislead viewers and possibly cause instability across a range of businesses, it poses a severe threat to modern society. These days, detecting such modified content is crucial to maintaining the trustworthiness and integrity of digital media. In response, this research proposes a robust deep learning-based technique for detecting deep fakes in videos. Our method uses the Deep fake Detection Challenge dataset, which contains both real and deep fake films, to train and assess our deep learning model. We want to provide a dependable solution that can differentiate between artificially generated information and actual content using stateof-the-art neural networks. Artificial intelligence-generated phony videos are more likely to spread thanks to deep learning algorithms, which raises serious concerns about official blackmail, terrorist propaganda, revenge pornography, and political manipulation. To alleviate these concerns, our approach is designed to automatically identify several forms of deep forgery, including replacement and re-enactment techniques. By means of extensive testing and analysis, we demonstrate the effectiveness of our deep fake detection system in precisely distinguishing bogus films from authentic ones. Our system, which combines cutting-edge deep learning methods with a large dataset, offers a promising way to lessen the hazards related to the spread of deep fake technologies

Deepfake, Disinformation and Social Media

Abstract: The advancement of Artificial Intelligence (AI) has uplifted the era of new possibilities. Along with the limelight positive aspects, there are some neglected negative aspects as well. With growing stardom, popularity and the need for power, AI and deep fakes have seen tremendous growth. Challenging the notions of privacy, identity and consent, AI clones and deepfake technology overshadow the reality and authenticity, producing fakely-realistic audiovisual contents. This paper deals with the impacts of AI clones and deepfaces, and lists out the possible solutions to reduce their impact.

Deepfake Detection using Deep Learning

The detection of deep fakes, which are synthetic media generated using deep learning techniques, has become increasingly important due to their potential to deceive and manipulate individuals, organizations, and society at large. This abstract explores recent advancements in deep fake detection methodologies, including machine learning algorithms, neural network architectures, and forensic techniques. Key challenges such as the rapid evolution of deep fake generation methods and the emergence of highly realistic forgeries are discussed. Additionally, the abstract examines the ethical implications of deep fake technology and the need for robust detection methods to mitigate its harmful effects. Finally, future directions in deep fake detection research are outlined, emphasizing the importance of interdisciplinary collaboration and the development of innovative approaches to combat this growing threat to information integrity and trust

Exposing Deep Fake Face Detection using LSTM and CNN

The rapid advancement of deep learning techniques, creating realistic multimedia content has become increasingly accessible, leading to the proliferation of DeepFake technology. DeepFake utilizes generative deep learning algorithms to produce or modify face features in a highly realistic manner, often making it challenging to differentiate between real and manipulated media. This technology, while beneficial in fields such as entertainment and education, also poses significant threats, including misinformation and identity theft. Consequently, detecting DeepFakes has become a critical area of research. In this paper, we propose a novel approach to DeepFake face detection by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. Our method leverages the strengths of CNNs in spatial feature extraction and LSTMs in temporal sequence modeling to enhance detection accuracy. The CNN component captures intricate facial features, while the LSTM analyzes the temporal dynamics of video frames. We evaluate our model on several benchmark datasets, including Celeb-DF (v2), DeepFake Detection Challenge Preview, and FaceForensics++. Experimental results demonstrate that our hybrid CNN-LSTM model achieves state-of-the-art performance, surpassing existing methods in both accuracy and robustness. This study highlights the potential of combining CNN and LSTM architectures for effective DeepFake detection, contributing to the ongoing efforts to safeguard against digital media manipulation

Identification of Counterfeit Videos using Deep Learning Methodology

The rise of deep learning has ushered in a proliferation of deep fake videos, posing significant challenges to the credibility of visual content. Our research introduces a groundbreaking approach by merging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance the accuracy of deepfake prediction. This unique integration, which has not been previously implemented, significantly boosts the model's capability to discern deepfakes. The synergy of CNNs and RNNs in our methodology represents an advancement, contributing to increased accuracy in detecting synthetic content. We leverage CNNs and RNNs for an efficient solution. First, we employ a Res-Next CNN to extract distinctive features from individual video frames, effectively encoding spatial information. These features are then used in the subsequent phase, where a LSTM-RNN models temporal dynamics within the videodata.The temporal aspect is crucial in differentiating deep fake videos due to subtle inconsistencies over time. The LSTM RNN processes the feature sequence, enabling the model to identify temporal patterns unique to deep fakes. This holistic approach, combining spatial and temporal analysis, enhances the model's ability to detect even highly convincing synthetic content. Our model is trained on a comprehensive dataset with rigorous evaluations, demonstrating competitive performance through standard metrics such as accuracy, precision. Practically, our model offers real- time video analysis, automatically identifying deep fake content and mitigating potential risks. Importantly, our approach is simple and robust, suitable for deployment across diverse scenarios. In summary, our research provides an effective solution to the critical issue of deepfake detection. By synergizing CNNs and LSTM-based RNNs, we offer a practical means to uphold the integrity of visual content in an era where digital information authenticity is paramount. Keywords— Deep Learning, CNN, RNN, Deepfake, LSTM, accuracy, precision, visual content, digital information

Ethics and Artifical intelligence – The rise of the deep fake

Ethics and Artifical intelligence – The rise of the deep fake

Deep Fake Video Detection

Deep learning algorithms have become so strong due to increasing processing power that it is now quite easy to produce an identical human-synthesized video, sometimes known as a "deep fake.". Scenarios where these realistic face swapped deep fakes are used to create political distress, fake terrorism events, revenge porn, blackmail peoples are easily envisioned. In this work, we describe a deep learning-based method that can effectively distinguish AI-generated fake videos from real videos. Our technique can recognize the replacement and reenactment automatically. deepfakes. We are trying to use Artificial Intelligence(AI) to fight Artificial Intelligence(AI). Our system uses a Res-Next Convolution n e u r a l network to extract the frame-level features and these features and further used to train the Long Short Term Memory(LSTM) based Recurrent Neural Network(RNN to classify whether the video is subject to any kind of manipulation or not, ie whether the video is deep fake or real video. In order to improve the model's performance on real-time data and replicate real-world circumstances, we assess our approach using a sizable, well-balanced, and diverse set of prepared data by mixing the various available data-set like FaceForensic++, Deepfake detection challenge, and Celeb-DF. We also show how our system achieved competitive result using very simple and robust approach

Consumer Protection and Deep Fakes - Assessing the Rights and Remedies for Victims in India

Abstract: In an era of fast technical improvement, the rise of deep fake technology poses a new and challenging problem for India's consumer protection. Deep fakes, a type of synthetic media developed with artificial intelligence algorithms, may modify audio, video, and images effectively, blurring the borders between truth and fabrication. Deep fakes can have far-reaching ramifications for consumers, including not only financial consequences but also social, psychological, and reputational harm. This topic's importance in the Indian setting cannot be emphasised. India, as a country with a diversified and large consumer base, is seeing an increase in digital contacts and transactions, making consumers increasingly vulnerable to the deceptive nature of deep fakes. Deep fakes pose a severe danger to consumer trust and confidence, ranging from intentionally manufactured marketing and counterfeit items to misinformation campaigns and identity theft.

Deep Neural Networks for Multimedia Forensics: Detection of Deepfake Content

Deep Neural Networks for Multimedia Forensics: Detection of Deepfake Content