2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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MLR-APPL-IP-5: Machine learning for image processing 5

Session Type: Poster
Time: Tuesday, September 21, 13:30 - 15:00
Location: Area E
Session Chair: Chee Seng Chan, University of Malaya
 
   MLR-APPL-IP-5.1: UNIVERSAL ADVERSARIAL ROBUSTNESS OF TEXTURE AND SHAPE-BIASED MODELS
         Kenneth Co; Imperial College London
         Luis Muñoz-González; Imperial College London
         Leslie Kanthan; DataSpartan
         Ben Glocker; Imperial College London
         Emil Lupu; Imperial College London
 
   MLR-APPL-IP-5.2: WEIGHTED AVERAGE PRECISION: ADVERSARIAL EXAMPLE DETECTION FOR VISUAL PERCEPTION OF AUTONOMOUS VEHICLES
         Weiheng Chai; Syracuse University
         Yantao Lu; Syracuse University
         Senem Velipasalar; Syracuse University
 
   MLR-APPL-IP-5.3: FABRICATE-VANISH: AN EFFECTIVE AND TRANSFERABLE BLACK-BOX ADVERSARIAL ATTACK INCORPORATING FEATURE DISTORTION
         Yantao Lu; Syracuse University
         Xueying Du; Northwestern Polytechnical University
         Bingkun Sun; Northwestern Polytechnical University
         Haining Ren; Purdue University
         Senem Velipasalar; Syracuse University
 
   MLR-APPL-IP-5.4: ADVERSARIAL TRAINING WITH STOCHASTIC WEIGHT AVERAGE
         Joong-Won Hwang; Electronics and Telecommunications Research Institute
         Youngwan Lee; Electronics and Telecommunications Research Institute
         Sungchan Oh; Electronics and Telecommunications Research Institute
         Yuseok Bae; Electronics and Telecommunications Research Institute
 
   MLR-APPL-IP-5.5: SIMTROJAN: STEALTHY BACKDOOR ATTACK
         Yankun Ren; Ant Group
         Longfei Li; Ant Group
         Jun Zhou; Ant Group
 
   MLR-APPL-IP-5.6: INTELLIGENT AND ADAPTIVE MIXUP TECHNIQUE FOR ADVERSARIAL ROBUSTNESS
         Akshay Agarwal; SUNY Buffalo
         Mayank Vatsa; Indian Institute of Technology Jodhpur
         Richa Singh; Indian Institute of Technology Jodhpur
         Nalini Ratha; SUNY Buffalo
 
   MLR-APPL-IP-5.7: IMPROVING FILLING LEVEL CLASSIFICATION WITH ADVERSARIAL TRAINING
         Apostolos Modas; École Polytechnique Fédérale de Lausanne (EPFL)
         Alessio Xompero; Queen Mary University of London
         Ricardo Sánchez-Matilla; Queen Mary University of London
         Pascal Frossard; École Polytechnique Fédérale de Lausanne (EPFL)
         Andrea Cavallaro; Queen Mary University of London
 
   MLR-APPL-IP-5.8: GENERATING ANNOTATED HIGH-FIDELITY IMAGES CONTAINING MULTIPLE COHERENT OBJECTS
         Bryan Cardenas Guevara; University of Amsterdam
         Devanshu Arya; University of Amsterdam
         Deepak K. Gupta; University of Amsterdam
 
   MLR-APPL-IP-5.9: A HYPERSPECTRAL APPROACH FOR UNSUPERVISED SPOOF DETECTION WITH INTRA-SAMPLE DISTRIBUTION
         Tomoya Kaichi; Keio University
         Yuko Ozasa; Tokyo Denki University
 
   MLR-APPL-IP-5.10: PART-BASED FEATURE SQUEEZING TO DETECT ADVERSARIAL EXAMPLES IN PERSON RE-IDENTIFICATION NETWORKS
         Yu Zheng; Syracuse University
         Senem Velipasalar; Syracuse University
 
   MLR-APPL-IP-5.11: SQUEEZE AND RECONSTRUCT: IMPROVED PRACTICAL ADVERSARIAL DEFENSE USING PAIRED IMAGE COMPRESSION AND RECONSTRUCTION
         Bo-Han Kung; Research Center for Information Technology Innovation, Academia Sinica
         Pin-Chun Chen; Research Center for Information Technology Innovation, Academia Sinica
         Yu-Cheng Liu; Research Center for Information Technology Innovation, Academia Sinica
         Jun-Cheng Chen; Research Center for Information Technology Innovation, Academia Sinica