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Introduction

Using bayesian optimization to generate adversarial examples that targets Object Detectors.

Get Started

Install Use Python>=3.7.0 for your conda environment or conda create --name [env_name] python=3.7. Then clone the repository and install the required packages as the following: 
$ git clone git@github.com:aho38/LLNL-BO-lightprojection-attack.git
$ cd LLNL-BO-lightprojection-attack
$ pip install -r requirements.txt
Bayesian Optimization Botorch is the main bayesian optimization package used in the code. You can find the specifications in bo_utils.py. In this version of the code. Only get_fitted_model is used but the steps are the same:

Adversarial Attack

Digital Attack with Perlin Noise

The file the runs digital attack with Perlin Noise on COCO Dataset

$ python main.py
Digital Attack parameters

There are numbers of parameters can be adjusted.

def parse_opt():
  parser = argparse.ArgumentParser(prog='val.py')
  parser.add_argument('--data', type=str, default='data/coco.yaml', help='dataset.yaml path')
  parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
  parser.add_argument('--batch-size', type=int, default=1, help='batch size')
  parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
  parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
  parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
  parser.add_argument('--task', default='val', help='train, val, test, speed or study')
  parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
  parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
  parser.add_argument('--augment', action='store_true', help='augmented inference')
  parser.add_argument('--verbose', action='store_true', help='report mAP by class')
  parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
  parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
  parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
  parser.add_argument('--save-json', action='store_false', help='save a COCO-JSON results file')
  parser.add_argument('--project', default='runs/testing', help='save to project/name')
  parser.add_argument('--name', default='exp', help='save to project/name')
  parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
  parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
  parser.add_argument('--att-type', type=str, default='mislabel-ml', help='types of attack')
  parser.add_argument('--start-count', type=int, default=0, help='number of start count usually 0-10')
  parser.add_argument('--num-count', type=int, default=5000, help='number of images that will be processed')
  parser.add_argument('--query-budget', type=int, default=20, help='number of queries we use')
  parser.add_argument('--norm', type=int, default=16, help='max norm of the image being perturbed')
  opt = parser.parse_args()
  opt.save_json |= opt.data.endswith('coco.yaml')
  opt.save_txt |= opt.save_hybrid
  opt.data = check_file(opt.data)  # check file
  return opt

Object Detect

$ python detect.py --source 0