Data-driven Verification of DNNs for Object Recognition

• URL: http://arxiv.org/abs/2408.00783v1
• Date: Wed, 17 Jul 2024 11:30:02 GMT
• Title: Data-driven Verification of DNNs for Object Recognition
• Authors: Clemens Otte, Yinchong Yang, Danny Benlin Oswan,
• Abstract summary: The paper proposes a new testing approach for Deep Neural Networks (DNN) using gradient-free optimization to find perturbation chains that successfully falsify the tested DNN. Applying it to an image segmentation task of detecting railway tracks in images, we demonstrate that the approach can successfully identify weaknesses of the tested DNN.
• Score: 0.20482269513546453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper proposes a new testing approach for Deep Neural Networks (DNN) using gradient-free optimization to find perturbation chains that successfully falsify the tested DNN, going beyond existing grid-based or combinatorial testing. Applying it to an image segmentation task of detecting railway tracks in images, we demonstrate that the approach can successfully identify weaknesses of the tested DNN regarding particular combinations of common perturbations (e.g., rain, fog, blur, noise) on specific clusters of test images.

Related papers

• SFOD: Spiking Fusion Object Detector [10.888008544975662]
  Spiking Fusion Object Detector (SFOD) is a simple and efficient approach to SNN-based object detection. We design a Spiking Fusion Module, achieving the first-time fusion of feature maps from different scales in SNNs applied to event cameras. We establish state-of-the-art classification results based on SNNs, achieving 93.7% accuracy on the NCAR dataset.
  arXiv  Detail & Related papers  (2024-03-22T13:24:50Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• A DPLL(T) Framework for Verifying Deep Neural Networks [9.422860826278788]
  Like human-written software, Deep Neural Networks (DNNs) can have bugs and can be attacked. We introduce NeuralSAT, a new verification approach that adapts the widely-used DPLL(T) algorithm used in modern SMT solvers.
  arXiv  Detail & Related papers  (2023-07-17T18:49:46Z)
• The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks [94.63547069706459]
  #DNN-Verification problem involves counting the number of input configurations of a DNN that result in a violation of a safety property. We propose a novel approach that returns the exact count of violations. We present experimental results on a set of safety-critical benchmarks.
  arXiv  Detail & Related papers  (2023-01-17T18:32:01Z)
• Segmentation of Roads in Satellite Images using specially modified U-Net CNNs [0.0]
  The aim of this paper is to build an image classifier for satellite images of urban scenes that identifies the portions of the images in which a road is located. Unlike conventional computer vision algorithms, convolutional neural networks (CNNs) provide accurate and reliable results on this task.
  arXiv  Detail & Related papers  (2021-09-29T19:08:32Z)
• Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features [8.723426955657345]
  We propose a novel abstraction method which abstracts a deep neural network and a dataset into a Bayesian network. We make use of dimensionality reduction techniques to identify hidden features that have been learned by hidden layers of the DNN. We can derive a runtime monitoring approach to detect in operational time rare inputs.
  arXiv  Detail & Related papers  (2021-03-05T14:28:42Z)
• DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers [16.893762648621266]
  We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations.
  arXiv  Detail & Related papers  (2021-03-02T10:41:16Z)
• D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization [108.8592577019391]
  Image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. We propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection.
  arXiv  Detail & Related papers  (2020-12-03T10:54:02Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)
• GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples [77.99182201815763]
  Deep neural networks (DNNs) are vulnerable to adversarial examples and other data perturbations. GraN is a time- and parameter-efficient method that is easily adaptable to any DNN. GraN achieves state-of-the-art performance on numerous problem set-ups.
  arXiv  Detail & Related papers  (2020-04-20T10:09:27Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.