The Collections 25 leak of 22 billion email addresses might have your

first_imgIn recent years, hackers have breached companies like Dropbox and LinkedIn by stealing 71 million and 117 million passwords, respectively. This month, Troy Hunt, security researcher identified the first portion of the data dump, named Collection #1, which has a set of breached databases. He represented 773 million unique usernames and passwords. Other researchers have now obtained and analyzed an additional vast database called Collections #2–5. It has 845 gigabytes of stolen data and 25 billion records in all. German news site Heise reported that Collection of 2.2 billion unique usernames and associated passwords has been distributed on hacker forums and torrents. According to the researchers at the Hasso Plattner Institute, 611 million credentials in Collections #2–5 weren’t included in the Collection #1 database. Chris Rouland, a cybersecurity researcher and founder of the IoT security firm, who also pulled Collections #1–5 from torrented files, said, “This is the biggest collection of breaches we’ve ever seen.” According to Rouland, as the collection has already been circulated amongst hackers, the tracker file which he downloaded was being seeded by more than 130 people who possessed the data dump. It has also been downloaded more than 1,000 times. In a statement to WIRED, Rouland said, “It’s an unprecedented amount of information and credentials that will eventually get out into the public domain.” According to WIRED, most of the stolen data appears to come from previous thefts, like the breaches of LinkedIn, Yahoo, and Dropbox. WIRED has examined a sample of the data and further confirmed that the credentials are valid, but mostly represent passwords from the previous years’ data leaks. This collection could be used as a powerful tool for unskilled hackers as they can try a technique called credential stuffing. With this technique, users can try previously leaked usernames and passwords on any website with the hope that people have reused passwords. Rouland said, “For the internet as a whole, this is still very impactful.” Who knows if we are targeted too? What should one do? Users can check for their usernames in the breach using Hasso Plattner Institute’s tool. This identity leak checker asks for users’ email address then uses that email ID to generate a list of information including users’ name, IP address, and password, if applicable. It tells the users if a password has been matched to their email address. It can also tell how recent that password actually is. One should change passwords for any breached sites it flags. It is advisable to not reuse passwords, and use a password manager. A password manager can automatically generate unique, secure passwords for the services a user uses. Users should turn on the two-factor authentication wherever possible. Though the two-factor authentication isn’t foolproof, it provides a layer of security. Troy Hunt’s service HaveIBeenPwned helps in checking if the passwords have been compromised, though it doesn’t yet include Collections #2-5. Read Next Internal memo reveals NASA suffered a data breach compromising employees social security numbers Former Senior VP’s take on the Mariott data breach; NYT reports suspects Chinese hacking ties Equifax data breach could have been “entirely preventable”, says House oversight and government reform committee staff reportlast_img read more

Researchers introduce a new generative model HoloGAN that learns 3D representation from

first_imgA group of researchers, namely, Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang, published a paper titled ‘HoloGAN: Unsupervised learning of 3D representations from natural images, last week. In the paper, researchers have proposed a generative adversarial network (GAN), called HoloGAN, for the task of unsupervised learning of 3D representations from natural images. HoloGAN works by adopting strong ‘inductive biases’ about the 3D world. The paper states that commonly used generative models depend on 2D Kernels to produce images and make assumptions about the 3D world. This is why these models tend to create blurry images in tasks that require a strong 3D understanding. HoloGAN, however, learns a 3D representation of the world and is successful at rendering this representation in a realistic manner. It can be trained using unlabelled 2D images without requiring pose labels, 3D shapes, or multiple views of the same objects. “Our experiments show that using explicit 3D features enables HoloGAN to disentangle 3D pose and identity, which is further decomposed into shape and appearance, while still being able to generate images with similar or higher visual quality than other generative models”, states the paper. How does HoloGAN work? HoloGAN first considers a 3D representation, which is later transformed to a target pose, projected to 2D features, and rendered to generate the final images. HoloGAN then learns perspective projection and rendering of 3D features from scratch with the help of a projection unit. Finally, to generate new views of the same scene, 3D rigid-body transformations are applied to the known 3D features, and the results are visualized using a neural renderer. This produces sharper results than performing 3D transformations in high-dimensional latent vector space. HoloGAN To learn 3D representations from 2D images without labels, HoloGAN extends the capability of traditional unconditional GANs by introducing a strong inductive bias about the 3D world into the generator network. During training, random poses from a uniform distribution are sampled and then the 3D features are transformed using these poses before they are rendered into images. Also, a variety of datasets are used to train HoloGAN, namely, Basel Face, CelebA, Cats, Chairs, Cars,  and LSUN bedroom. HoloGAN is trained on a resolution of 64×64 pixels for Cats and Chairs, and 128×128 pixels for Basel Face, CelebA, Cars and LSUN bedroom. Other than that, HoloGAN generates 3D representations using a learned constant tensor. The random noise vector instead is treated as a “style” controller and gets mapped to affine parameters for adaptive instance normalization (AdaIN) using a multilayer perceptron. Results and Conclusion In the paper, researchers prove that HoloGAN can generate images with comparable or greater visual fidelity than other 2D-based GAN models. HoloGAN can also learn to disentangle challenging factors in an image, such as 3D pose, shape, and appearance. The paper also shows that HoloGAN can successfully learn meaningful 3D representations across different datasets with varying levels of complexity. “We are convinced that explicit deep 3D representations are a crucial step forward for both the interpretability and controllability of GAN models, compared to existing explicit or implicit 3D representations”, reads the paper. However, researchers state that while HoloGAN is successful at separating pose from identity, its performance largely depends on the variety and distribution of poses included in the training dataset. The paper cites an example of the CelebA and Cats dataset where the model cannot recover elevation as well as azimuth. This is due to the fact that most face images are taken at eye level, thereby, containing limited variation in elevation. Future work The paper states that researchers would like to explore learning distribution of poses from the training data in an unsupervised manner for uneven pose distributions. Other directions that can be explored include further disentanglement of objects’, and appearances ( texture and illumination). Researchers are also looking into combining the HoloGAN with training techniques suchas progressive GANs to produce higher-resolution images. For more information, check out the official research paper. Read Next DeepMind researchers provide theoretical analysis on recommender system, ‘echo chamber’ and ‘filter bubble effect’ Google AI researchers introduce PlaNet, an AI agent that can learn about the world using only images Facebook researchers show random methods without any training can outperform modern sentence embeddings models for model classificationlast_img read more