49 research outputs found
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
Detecting fake images is becoming a major goal of computer vision. This need
is becoming more and more pressing with the continuous improvement of synthesis
methods based on Generative Adversarial Networks (GAN), and even more with the
appearance of powerful methods based on Diffusion Models (DM). Towards this
end, it is important to gain insight into which image features better
discriminate fake images from real ones. In this paper we report on our
systematic study of a large number of image generators of different families,
aimed at discovering the most forensically relevant characteristics of real and
generated images. Our experiments provide a number of interesting observations
and shed light on some intriguing properties of synthetic images: (1) not only
the GAN models but also the DM and VQ-GAN (Vector Quantized Generative
Adversarial Networks) models give rise to visible artifacts in the Fourier
domain and exhibit anomalous regular patterns in the autocorrelation; (2) when
the dataset used to train the model lacks sufficient variety, its biases can be
transferred to the generated images; (3) synthetic and real images exhibit
significant differences in the mid-high frequency signal content, observable in
their radial and angular spectral power distributions
Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
The Video and Image Processing (VIP) Cup is a student competition that takes
place each year at the IEEE International Conference on Image Processing. The
2022 IEEE VIP Cup asked undergraduate students to develop a system capable of
distinguishing pristine images from generated ones. The interest in this topic
stems from the incredible advances in the AI-based generation of visual data,
with tools that allows the synthesis of highly realistic images and videos.
While this opens up a large number of new opportunities, it also undermines the
trustworthiness of media content and fosters the spread of disinformation on
the internet. Recently there was strong concern about the generation of
extremely realistic images by means of editing software that includes the
recent technology on diffusion models. In this context, there is a need to
develop robust and automatic tools for synthetic image detection
Scritter A multiplexed image system for a public screen
Abstract -Scritter is a system that enables one the superimposition of invisible messages and comments on a large screen while sharing a movie. By putting other information on an image that only users who wear special glasses (named "IP(Information Polarized)-Glasses") can see, a multiplex of image media can be realized. By selecting the glasses, visible images can be changed into a movie or a message
HairBrush for Immersive Data-Driven Hair Modeling
International audienceWhile hair is an essential component of virtual humans, it is also one of the most challenging digital assets to create. Existing automatic techniques lack the generality and flexibility to create rich hair variations, while manual authoring interfaces often require considerable artistic skills and efforts, especially for intricate 3D hair structures that can be difficult to navigate. We propose an interactive hair modeling system that can help create complex hairstyles in minutes or hours that would otherwise take much longer with existing tools. Modelers, including novice users, can focus on the overall hairstyles and local hair deformations, as our system intelligently suggests the desired hair parts. Our method combines the flexibility of manual authoring and the convenience of data-driven automation. Since hair contains intricate 3D structures such as buns, knots, and strands, they are inherently challenging to create using traditional 2D interfaces. Our system provides a new 3D hair author-ing interface for immersive interaction in virtual reality (VR). Users can draw high-level guide strips, from which our system predicts the most plausible hairstyles via a deep neural network trained from a professionally curated dataset. Each hairstyle in our dataset is composed of multiple variations, serving as blend-shapes to fit the user drawings via global blending and local deformation. The fitted hair models are visualized as interactive suggestions that the user can select, modify, or ignore. We conducted a user study to confirm that our system can significantly reduce manual labor while improve the output quality for modeling a variety of head and facial hairstyles that are challenging to create via existing techniques
Generative Novel View Synthesis with 3D-Aware Diffusion Models
We present a diffusion-based model for 3D-aware generative novel view
synthesis from as few as a single input image. Our model samples from the
distribution of possible renderings consistent with the input and, even in the
presence of ambiguity, is capable of rendering diverse and plausible novel
views. To achieve this, our method makes use of existing 2D diffusion backbones
but, crucially, incorporates geometry priors in the form of a 3D feature
volume. This latent feature field captures the distribution over possible scene
representations and improves our method's ability to generate view-consistent
novel renderings. In addition to generating novel views, our method has the
ability to autoregressively synthesize 3D-consistent sequences. We demonstrate
state-of-the-art results on synthetic renderings and room-scale scenes; we also
show compelling results for challenging, real-world objects.Comment: Project page: https://nvlabs.github.io/genv
Pengaruh Komunikasi Terapeutik Perawat Terhadap Kepuasan Pasien Di Rawat Jalan RSUD Jogja
The Objective of this study is to know influence of nurse therapeutic communication to satisfaction of patients satisfaction in RSUD Yogyakarta. The study was a quantitative research methods such as surveys of descriptive inferential research with cross sectional approach. Number of samples in this research is 285 sample in inpatient and 140 in emergency room. The instrument used a questionnaire. Analysis of data using multiple linear regression. This study show that there is the influence of therapeutic communication nurse to satisfaction of outpatients and Emergency room in RSUD Yogyakarta, and orientation phase is a phase that most influence on patient satisfaction. The most influential to therapeutic communication is termination stage