Larry S. Davis

ICLR Conference 2022 Conference Paper

Responsible Disclosure of Generative Models Using Scalable Fingerprinting

• Ning Yu 0006
• Vladislav Skripniuk
• Dingfan Chen
• Larry S. Davis
• Mario Fritz

Over the past years, deep generative models have achieved a new level of performance. Generated data has become difficult, if not impossible, to be distinguished from real data. While there are plenty of use cases that benefit from this technology, there are also strong concerns on how this new technology can be misused to generate deep fakes and enable misinformation at scale. Unfortunately, current deep fake detection methods are not sustainable, as the gap between real and fake continues to close. In contrast, our work enables a responsible disclosure of such state-of-the-art generative models, that allows model inventors to fingerprint their models, so that the generated samples containing a fingerprint can be accurately detected and attributed to a source. Our technique achieves this by an efficient and scalable ad-hoc generation of a large population of models with distinct fingerprints. Our recommended operation point uses a 128-bit fingerprint which in principle results in more than 10^{38} identifiable models. Experiments show that our method fulfills key properties of a fingerprinting mechanism and achieves effectiveness in deep fake detection and attribution. Code and models are available at https://github.com/ningyu1991/ScalableGANFingerprints.

AAAI Conference 2022 Conference Paper

Rethinking Pseudo Labels for Semi-supervised Object Detection

• Hengduo Li
• Zuxuan Wu
• Abhinav Shrivastava
• Larry S. Davis

Recent advances in semi-supervised object detection (SSOD) are largely driven by consistency-based pseudo-labeling methods for image classification tasks, producing pseudo labels as supervisory signals. However, when using pseudo labels, there is a lack of consideration in localization precision and amplified class imbalance, both of which are critical for detection tasks. In this paper, we introduce certainty-aware pseudo labels tailored for object detection, which can effectively estimate the classification and localization quality of derived pseudo labels. This is achieved by converting conventional localization as a classification task followed by refinement. Conditioned on classification and localization quality scores, we dynamically adjust the thresholds used to generate pseudo labels and reweight loss functions for each category to alleviate the class imbalance problem. Extensive experiments demonstrate that our method improves state-of-the-art SSOD performance by 1-2% AP on COCO and PASCAL VOC while being orthogonal and complementary to most existing methods. In the limited-annotation regime, our approach improves supervised baselines by up to 10% AP using only 1–10% labeled data from COCO.

AAAI Conference 2020 Conference Paper

Universal Adversarial Training

• Ali Shafahi
• Mahyar Najibi
• Zheng Xu
• John Dickerson
• Larry S. Davis
• Tom Goldstein

Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels. In contrast, a universal perturbation is an update that can be added to any image in a broad class of images, while still changing the predicted class label. We study the efficient generation of universal adversarial perturbations, and also efficient methods for hardening networks to these attacks. We propose a simple optimization-based universal attack that reduces the top-1 accuracy of various network architectures on ImageNet to less than 20%, while learning the universal perturbation 13× faster than the standard method. To defend against these perturbations, we propose universal adversarial training, which models the problem of robust classifier generation as a two-player min-max game, and produces robust models with only 2× the cost of natural training. We also propose a simultaneous stochastic gradient method that is almost free of extra computation, which allows us to do universal adversarial training on ImageNet.

ICML Conference 2013 Conference Paper

Predictable Dual-View Hashing

• Mohammad Rastegari
• Jonghyun Choi
• Shobeir Fakhraei
• Hal Daumé III
• Larry S. Davis

We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.

ICRA Conference 2009 Conference Paper

Assigning cameras to subjects in video surveillance systems

• Hazem El-Alfy
• David W. Jacobs
• Larry S. Davis

We consider the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time, with camera switches when needed. The problem is modeled as a bipartite graph and the solution corresponds to a maximum matching. As people move, the solution is efficiently updated by computing an augmenting path rather than by solving for a new matching. This reduces computation time by an order of magnitude. In addition, solving for the shortest augmenting path minimizes the number of camera switches at each update. When not all people can be covered by the available cameras, we cluster as many people as possible into small groups, then assign cameras to groups using a minimum cost matching algorithm. We test our method using numerous runs from different simulators.

ICRA Conference 2008 Conference Paper

Human detection using iterative feature selection and logistic principal component analysis

• Wael Abd-Almageed
• Larry S. Davis

We present a fast feature selection algorithm suitable for object detection applications where the image being tested must be scanned repeatedly to detected the object of interest at different locations and scales. The algorithm iteratively estimates the belongness probability of image pixels to foreground of the image. To prove the validity of the algorithm, we apply it to a human detection problem. The edge map is filtered using a feature selection algorithm. The filtered edge map is then projected onto an eigen space of human shapes to determine if the image contains a human. Since the edge maps are binary in nature, Logistic Principal Component Analysis is used to obtain the eigen human shape space. Experimental results illustrate the accuracy of the human detector.

IROS Conference 2006 Conference Paper

Tracking Articulating Objects from Ground Vehicles using Mixtures of Mixtures

• Wael Abd-Almageed
• Mohamed E. Hussein 0001
• Larry S. Davis

An algorithm for tracking articulating objects from moving camera platforms is presented. Mixtures of mixtures are used to model the appearance of the object and the background. The state of the object is tracked using a particle filter. Egomotion information are estimated and used to set the state variance of the particle filter. Results of tracking human objects from an unmanned ground vehicle are used to evaluate the tracking algorithm

ICRA Conference 1992 Conference Paper

Navigation with uncertainty: reaching a goal in a high collision risk region

• Philippe Burlina
• Daniel DeMenthon
• Larry S. Davis

The authors describe a computational framework in which a probabilistic method for noisy sensor-based robotic navigation in dynamic environments can be devised. The aim of the method is to generate an optimal trajectory by considering as optimality criteria the probability of not colliding with the obstacles and the probability of accessing an operational position with respect to a moving target object. A formal framework in which the probability of collision associated with an elementary robot displacement can be calculated is discussed. Estimates on the obstacle kinematic parameters and measures of confidence on these estimates are used to produce the probability of collision associated with any robot displacement. The probability of collision is derived in two steps: a stochastic model is defined in the kinematic state space of the obstacles and collision events are given a simple geometric characterization in this state space. >

ICRA Conference 1990 Conference Paper

Fast range scanner using an optic RAM

• Tsutomu Ito
• Daniel DeMenthon
• Larry S. Davis

A range scanner which calculates ranges by triangulation between the incident angles of laser stripes and the positions of their images on a camera sensor was developed for robotic applications. It uses a solid-state image sensor called Optic RAM instead of a CCD sensor. This sensor chip has three desirable characteristics for the position detection of laser stripes in images: it thresholds the image, detecting only the brighter stripes in binary form; it is an image memory; and pixel values can be addressed randomly in the image. Thus, the design does not require an A/D converter or a frame buffer and is consequently inexpensive. For improved performance, only the image region next to the previous stripe location is searched, and a 64-KB lookup table stored in RAM is indexed by incident laser angles and stripe addresses to output range data. A 128*256 range image is produced in about 20 s. This is reasonably fast considering that this process requires analyzing 256 images. The speed bottleneck is the low sensitivity of the optic RAM chip, which requires a long exposure time per frame (60 ms), corresponding to half the standard video frame rate. Simple calibration methods using planar patterns of parallel lines are presented. >

ICRA Conference 1990 Conference Paper

New exact and approximate solutions of the three-point perspective problem

• Daniel DeMenthon
• Larry S. Davis

An exact method for computing the position of a triangle in space from its image is presented. Also presented is an approximate method based on orthoperspective, an approximation of perspective which produces lower errors for off-center triangle images than scaled orthographic projection. A comparison is made of exact and approximate solutions for the triangle pose. This comparison gives the relative combinations of image and triangle characteristics which are likely to generate the largest errors. Model-based pose estimation techniques which match image and model triangles require large numbers of matching operations in real-world applications. It is shown that the approximate model can be used to build lookup tables for each of the triangles of a model and that they speed up the estimation of an object pose. >

ICRA Conference 1990 Conference Paper

Reconstruction of a road by local image matches and global 3D optimization

• Daniel DeMenthon
• Larry S. Davis

A method is presented for reconstructing a 3-D road from a single image. It finds the images of opposite points of the road. Opposite points are points which face each other on the opposite sides of the road; the images of these points are called matching points. For points chosen from one side of the road image, the algorithm finds all the matching point candidates on the other side, based on local properties of a road. However, these solutions do not necessarily satisfy the global properties of a typical road. A dynamic programming algorithm is applied to reject the candidates which do not fit the global road. A benchmark using synthetic roads is described. It shows that the roads reconstructed by the proposed method match the actual roads better than those reconstructed by two other road reconstruction algorithms. Experiments with 50 road images taken by the autonomous land vehicle (ALV) showed that the method is robust with real-world data and that the reconstructions are fairly consistent with road profiles obtained by fusion between range images and video images. >

ICRA Conference 1986 Conference Paper

A visual navigation system

• Allen M. Waxman
• Jacqueline Le Moigne
• Larry S. Davis
• Eli Liang
• T. Siddalingaiah

A modular system architecture has been developed to support visual navigation by an autonomous land vehicle. The system consists of vision modules performing image processing, 3-D shape recovery, and rule-based reasoning, as well as modules for planning, navigating and piloting. The system runs in two distinct modes, bootstrap and feed-forward. The bootstrap mode requires analysis of entire images in order to find and model the objects of interest in the scene (e. g. roads). In the feed-forward mode (while the vehicle is moving), attention is focused on small parts of the visual field as determined by prior views of the scene, in order to continue to track and model the objects of interest. We have decomposed general navigational tasks into three categories, all of which contribute to planning a vehicle path. They are called long, intermediate and short range navigation, reflecting the scale to which they apply. We have implemented the system as a set of concurrent, communicating modules and use it to drive a camera (carried by a robot arm) over a scale model road network on a terrain board.

ICRA Conference 1985 Conference Paper

Visual algorithms for autonomous navigation

• Fred P. Andresen
• Larry S. Davis
• Roger D. Eastman
• Subbarao Kambhampati

The Computer Vision Laboratory at the University of Maryland is designing and developing a vision system for autonomous ground navigation. Our approach to visual navigation segments the task into three levels called long range, intermediate range and short range navigation. At the long range, one would first generate a plan for the day's outing, identifying the starting location, the goal, and a low resolution path for moving from the start to the goal. From time to time, during the course of the outing, one may want to establish his position with respect to the long range plan. This could be accomplished by visually identifying landmarks of known location, and then triangulating to determine current position. We describe a vision system for position determination that we have developed as part of this project. At the intermediate range, one would look ahead to determine generally safe directions of travel called corridors of free space. Short range navigation is the process that, based on a detailed topographic analysis of one's immediate environment, enables us to safely navigate around obstacles in the current corridor of free space along a track of safe passage. We describe a quadtree based path planning algorithm which could serve as the basis for identifying such tracks of safe passage.

AIJ Journal 1981 Journal Article

Cooperating processes for low-level vision: A survey

• Larry S. Davis
• Azriel Rosenfeld