Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios.
To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this…
MARE: Multimodal Alignment and Reinforcement for Explainable Deepfake Detection via Vision-Language Models
School of Computer Science and Engineering, MoE Key Laboratory of Information Technology, Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China（计算机科学与工程学院，信息技术MOE实验室，广东省信息安全技术重点实验室，中山大学，广州510006，中国）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Department of Computer and Information Science, University of Macau.（计算机与信息科学系，澳门大学）
Advances in AI based computer vision has led to a significant growth in synthetic image generation and artificial image tampering with serious implications for unethical exploitations that undermine person identification and could make render AI predictions less explainable.Morphing, Deepfake and other artificial generation of face photographs undermine the reliability of face biometrics authentication using different electronic ID documents.Morphed face photographs on e-passports can fool automated border control systems and human guards.This paper extends our previous work on using the persi…
EDVD-LLaMA: Explainable Deepfake Video Detection via Multimodal Large Language Model Reasoning
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（电子与电气工程系，香港理工大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程学院，华南理工大学）
X2-DFD: A framework for eXplainable and eXtendable Deepfake Detection
School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P.R. China（1 数据科学学院，香港中文大学（深圳））；School of Electronic and Computer Engineering, Peking University, P.R. China（2 电子与计算机工程学院，北京大学）；Department of Computer Science, University of Liverpool, Liverpool, L69 7ZX, UK（3 计算机科学系，利物浦大学）；Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA（4 计算机科学与工程系，布法罗大学）
EvolveReason: Self-Evolving Reasoning Paradigm for Explainable Deepfake Facial Image Identification
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Ant Group（蚂蚁集团）
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
University of Southampton（南安普顿大学）；Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；Shenzhen Institute of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）
DeepFake Detection in Dyadic Video Calls using Point of Gaze Tracking
Department of Computer Science, Clarkson University, Potsdam, NY 13699 USA (e-mail: )；Department of Electrical \& Computer Engineering, Clarkson University, Potsdam, NY 13699 USA (e-mail: )
Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes
Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）；Shenzhen Key Laboratory of Media Security（深圳媒体安全重点实验室）；SZU-AFS Joint Innovation Center for AI Technology（深圳大学-腾讯优图联合创新中心）；Shenzhen University（深圳大学）；School of Electronic and Computer Engineering（电子与计算机工程学院）；Peking Univerisity（北京大学）；Tencent Youtu Lab（腾讯优图实验室）
Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies
USC Information Sciences Institute（南加州大学信息科学研究所）；USC Ming Hsieh Department of Electrical and Computer Engineering（南加州大学明氏电气与计算机工程学院）；Clemson University（克莱姆森大学）；Holcombe Department of Electrical and Computer Engineering（霍尔科姆电气与计算机工程学院）
FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东省超高清沉浸式媒体技术重点实验室）；School of Future Technology, South China University of Technology（华南理工大学未来技术学院）
Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection
Nanyang Technological University（南洋理工大学）；Ant Digital Technologies, Ant Group（蚂蚁数字科技，蚂蚁集团）；Singapore University of Technology and Design（新加坡科技设计大学）；Singapore Management University（新加坡管理大学）
University of Maryland College Park（马里兰大学帕克分校）；Johns Hopkins University（约翰斯·霍普金斯大学）
Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and gi…
