'The Beatles: Get Back' shows that deepfake tech isn't always evil


While it rarely happens for me, I occasionally observe something that is an actual watershed moment for technological advancement and can acknowledge that the possibility for good using said technologies might outweigh the potential for evil. Streaming your favorite shows has never been easier. For me, such a moment was watching The Beatles: Get Back, which is now airing on Disney . I had heard about it and seen some of its footage slowly being released for about a year, but I had no idea just how much technology had been applied to its production. I've seen many films made from historical footage from the 1960s and even the 1970 and 1980s, and so much of the source material is in poor condition.

DeepFake Detection


Technology is ever evolving, and IT IS everywhere. It is very possible that you might have come across the word "DeepFake" at some point. "DeepFake" is one of the creations of technology which is relatively new, very unique, powerful AND dangerous if used by wrong hands. The term DeepFake referred the face swapped and morphed images of celebrities posted on the sub-reddit called "r/deepfakes". It was coined after a reddit user with the "Deepfakes" in 2017 who posted the deepfakes he created as well as the other members of the sub-reddit.

Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming


Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent s...

Disentanglement Is the Next Deepfake Revolution


CGI data augmentation is being used in a new project to gain greater control over deepfake imagery. Though you still can't effectively use CGI heads to fill in the missing gaps in deepfake facial datasets, a new wave of research into disentangling identity from context means that soon, you may not have to. The creators of some of the most successful viral deepfake videos of the past few years select their source videos very carefully, avoiding sustained profile shots (i.e. the kind of side-on mugshots popularized by police arrest procedures), acute angles and unusual or exaggerated expressions. Increasingly, the demonstration videos produced by viral deepfakers are edited compilations which select the'easiest' angles and expressions to deepfake. In fact, the most accommodating target video in which to insert a deepfaked celebrity is one where the original person (whose identity will be erased by the deepfake) is looking straight to camera, with a minimal range of expressions.

Deep diving into deepfakes


The weaponisation of deepfakes against politicians or nation states has become something we're simply going to have to live with,

Seoul shares face biometrics of 170M travelers with private firms


The South Korean government shared roughly 170 million face images of citizens and resident foreign nationals with the private sector without their consent to be used in training and testing biometric algorithms, according to a recent Ministry of Justice document. The move is part of an "AI identification and tracking system development project" based on a memorandum of understanding between the Korean Ministry of Justice (MOJ) and the Ministry of Science and ICT (MSIT). Scheduled for completion in 2022, the project has seen the MOJ transferring information obtained during the immigration screening process to the MSIT, including face biometrics, nationality, gender, and age. The MSIT subsequently transferred that information to private businesses for the purpose of artificial intelligence technology research, according to the allegations. The South Korean government mentioned the creation of the project in a press release when it first launched in 2019 but did not disclose information about its structure, scope, or data collection methods.

Deepfake -- When seeing is no longer believing


How many times do you see a video of famous personalities saying something'strange' which you believe they have would not say? How many times you have seen some false information spread from a trusted source? Are we going to see the movie Face/Off starring Nicolas Cage & John Travolta becoming a reality? Welcome to the era of fake news/fake videos or in technical terms "Deepfake" and It's the term you are going to hear more often in the near future. Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud.

Towards General Deep Leakage in Federated Learning Artificial Intelligence

Unlike traditional central training, federated learning (FL) improves the performance of the global model by sharing and aggregating local models rather than local data to protect the users' privacy. Although this training approach appears secure, some research has demonstrated that an attacker can still recover private data based on the shared gradient information. This on-the-fly reconstruction attack deserves to be studied in depth because it can occur at any stage of training, whether at the beginning or at the end of model training; no relevant dataset is required and no additional models need to be trained. We break through some unrealistic assumptions and limitations to apply this reconstruction attack in a broader range of scenarios. We propose methods that can reconstruct the training data from shared gradients or weights, corresponding to the FedSGD and FedAvg usage scenarios, respectively. We propose a zero-shot approach to restore labels even if there are duplicate labels in the batch. We study the relationship between the label and image restoration. We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images. Our approaches are evaluated on classic image benchmarks, including CIFAR-10 and ImageNet. The batch size, image quality, and the adaptability of the label distribution of our approach exceed those of GradInversion, the state-of-the-art.

The Hidden Second Face of Deepfakes


A lot of times when you read about deepfakes (more professionally known as synthetic media) the common themes being explored is only one of the two faces of deepfakes the negative side, however, I want to explore some of the positive things deepfakes can be used for so you can get a full scope of the capabilities of deepfakes. Glad you asked, simple answer: artificial intelligence-generated media that has seamlessly stitch anyone in the world into a video or photo they never actually in and a summarised more technical answer: deepfakes are made by using a GAN (generative adversarial network) a type of deep learning artificial intelligence. It uses two neural networks that rival each other to generate a synthetic version of data that can pass for real data, one of the neural networks is called the generator (generates new data instances) and the other is called a discriminator (evaluates them for authenticity). The purpose of the generator is to generate synthetic media that is given to the discriminator, which its purpose is to identify whether the media is fake or real, they are trained together until it achieves acceptable accuracy (discriminator fooled 50% of the time). So now that we have a better understanding of how deepfakes are created we can begin to explore the positive uses of deepfakes.

Ego4D: Around the World in 3,000 Hours of Egocentric Video Artificial Intelligence

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,025 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: