Daisy-Zhang/Awesome-Deepfakes-Detection

Detection of Deepfake Videos and Audios on Social Media Platforms

Deepfake technology, a rapidly evolving application of artificial intelligence, has enabled the creation of highly realistic yet synthetic multimedia content. While this innovation offers potential benefits in areas such as entertainment and education, its misuse has raised significant ethical and security concerns, including misinformation and financial fraud. This study evaluates the effectiveness of current deepfake detection methods, focusing on the Xception model for video detection and the LCNN model for audio detection, using a dataset composed of real-life and deepfake content. The dataset includes deepfakes generated by tools such as the Deepfake Offensive Toolkit and Haotian AI, a cutting-edge provider known for its high-quality outputs. Our findings reveal that the Xception model, while achieving 89.1% accuracy on control datasets, struggled to detect Haotian AI-generated deepfakes, misclassifying nearly all samples as authentic. This performance gap highlights the need for more diverse training datasets and advanced detection frameworks capable of addressing the nuances of emerging deepfake tools. Additionally, metadata changes caused by uploading and downloading content on social media platforms were found to have minimal impact on detection accuracy, challenging the feasibility of metadata-based detection approaches. This research underscores the limitations of current deepfake detection models and emphasizes the necessity for multimodal approaches and broader datasets to enhance robustness. The study’s implications call for continued advancements in detection methods to keep pace with the growing sophistication of deepfake technologies.

AI Detector — Verified AI Content Checker | Pangram

Dive into the technical foundations behind Pangram's AI detection stack and understand how we achieve low false positive rates across modalities.

SkyLink - Bluesky DID Detector – Get this Extension for 🦊 Firefox (en-US)

Download SkyLink - Bluesky DID Detector for Firefox. Detects Decentralized Identifiers (DIDs) in a domain's TXT records and links to the associated Bluesky profile.

‘HELLO BOSS’: Inside the Chinese Realtime Deepfake Software Powering Scams Around the World

404 Media has obtained a copy of ‘Haotian AI’, a popular piece of realtime deepfake software marketed to scammers. It can turn a fraudster's face into anyone else's on WhatsApp, Zoom, and Teams.

ImageWhisperer — AI Image Detector

Upload an image. Get the investigation. 41 checks, one verdict, plain-language evidence. By Henk van Ess.

Google joins AI watermarking coalition as deepfakes hit mainstream tech platforms

Google said it will look to use a project from Adobe called Content Credentials, which adds metadata that indicates AI editing and allows viewers to verify images, videos, audio and documents.

Jacky Kwok on Twitter / X

Scaling self-verification with DeepSeek V4 Flash beats Claude Fable 5 on Terminal-Bench 2.1, while being 11x cheaper 💰As open-source models become more capable, they can now generate large numbers of high-quality candidate solutions and verify their own outputs at very low… https://t.co/as2HtyHzzW pic.twitter.com/XzVBgr5JPz— Jacky Kwok (@jackyk02) August 17, 2026

DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index, 10 points above previous DeepSeek V4 Flash

DeepSeek releases DeepSeek V4 Flash 0731

Dynamic 1-bit DeepSeek-R1-0528 GGUFs out now!

118 votes, 16 comments. Hey guys sorry for the wait, but now you can now run DeepSeek-R1-0528 with our Dynamic 1-bit GGUFs!…

Deepfake detection by human crowds, machines, and machine-informed crowds

Significance The recent emergence of deepfake videos raises theoretical and practical questions. Are humans or the leading machine learning model more capable of detecting algorithmic visual manipulations of videos? How should content moderation systems be designed to detect and flag video-based misinformation? We present data showing that ordinary humans perform in the range of the leading machine learning model on a large set of minimal context videos. While we find that a system integrating human and model predictions is more accurate than either humans or the model alone, we show inaccurate model predictions often lead humans to incorrectly update their responses. Finally, we demonstrate that specialized face processing and the ability to consider context may specially equip humans for deepfake detection. , The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

Deepfake detection by human crowds, machines, and machine-informed crowds

Significance The recent emergence of deepfake videos raises theoretical and practical questions. Are humans or the leading machine learning model more capable of detecting algorithmic visual manipulations of videos? How should content moderation systems be designed to detect and flag video-based misinformation? We present data showing that ordinary humans perform in the range of the leading machine learning model on a large set of minimal context videos. While we find that a system integrating human and model predictions is more accurate than either humans or the model alone, we show inaccurate model predictions often lead humans to incorrectly update their responses. Finally, we demonstrate that specialized face processing and the ability to consider context may specially equip humans for deepfake detection. , The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

deepseek-ai/DeepSeek-V4-Flash-0731 at 9e165c30e2704aec5d9d593cce3eebd58bbef1cb

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

deepseek-ai/DeepSeek-OCR-2 · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security — California Law Review

Harmful lies are nothing new. But the ability to distort reality has taken an exponential leap forward with “deep fake” technology. This capability makes it possible to create audio and video of real people saying and doing things they never said or did. Machine learning techniques are escalating th