1st and 2nd FM generation 3D halftoning1 MICRON PIXELS133-MEGAPIXEL180 degree images1-bit matrix completion1/f noise108 Megapixel100 Hue test
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2.5D reconstruction2D VIEWS2D DCT2D to Hologram conversion2-d scale2D metrics2D and 3D video2D-plus-depth video2.5 D printing2.5D PRINTING2D/3D imaging, high performance computing, imaging systems, efficient computations and storage2x2 On-chip Lens2.5D printing2D AND 3D CONVERTIBLE DISPLAY2.5D2D2AFC2D-TO-3D CONVERSION ARTIFACTS2-D barcodes2D printing
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3D TRANSFORMATION3D model3D compression3D Imaging360-degree Image3D Range Data3D surface reproduction3D STACK3D Modeling360-degree videos3D scene flow estimation3D connected tube model3D Video Communications3D Shape Indexing and Retrieval3D/4D DATA PROCESSING AND FILTERING3D EDUCATIONAL MATERIAL3D Tracking3D image compression3D Digital Image Correlation3D Halftoning3D Data Sources3D camera3D-HEVC3D adaptive halftoning3D printing3D Communications360-degree content3D shape3D PRINTER360 degree images3D Range Data Compression3D Mapping3D scanning3D capture3D modelling3D recovery3D Printing3D Point Cloud3D affine transformation3D skeletal joint point cloud; deep learning; PointNet++ method3D/2D Visuals3d scanning3D modeling3D rigid transformations3D RECONSTRUCTION3D digital halftoning3D Vision3D Immersion360 IMAGING3D HALFTONING3D range scanning3D Curvelet3ARRI footage360° video3D depth sensing3D shape indexing and retrieval3D mesh3D-color perception3D display3D refinement3D Measurement3D theater program listing3DLUT3D DIGITIZATION METHOD FOR OIL PAINTINGS3D position measurement of people3D-shooting3D Data Processing3D image3D Telepresence3D Morphable Model3D Object Detection360-degree images3D PRINTING3D face alignment3D MODEL3D/4D Scanning3D video3D-LUT3D CAMERAS360 degrees video360-degree art exhibition360-degree image projection3D stereo vision360 panorama3D perception3D mapping and localization3D/4D Data Processing and Filtering360VR360-degree video streaming3D depth-map360-video3D Gaussian Splatting3D Saliency3D surface3D data processing3D optical scans3D video processing3D scene classification3D warping3D objects360-degree3D MESHES3D Display3D Gaussian splatting3DSR360-degree video3D communications3DViewers3D displays360° STEREO PANORAMAS3D Iterative Halftoning3D MESH3D Lidar3D Scene Reconstruction3D-CNN3D Compression and Encryption3D VISUALIZATION3DMM3D reconstruction3D SALIENCY3D Meshes3D-human body detection3D recursive search3-T pixel3D Video3D print3D halftoning3D and 2D3D SCENE RECONSTRUCTION AND MODELING3D SHAPE INDEXING AND RETRIEVAL3D shape analysis360-DEGREE IMAGE360-deg quality assessment3D Video Conferencing3D Quality3D RANGE IMAGING3D Image Processing3d localization3D MODELLING3-D RECONSTRUCTION3D Models3D object shape3D Compression3D Computer Graphics3D Scene Reconstruction and Modeling3D-Anisotropic smoothing3DCNN3D human-centered technologies3D point cloud3D projector3D mesh simplification35MM FILM DIGITIZATION3D/4D SCANNING3D localization3D-assisted features3D colour Digital Image Correlation3D Print Appearance3D localization and mapping3D digitization and dissemination3D range geometry3D INTERACTION360° VIDEO3D ACQUISITION ARCHITECTURE3D audio3D imaging3D RECOVERY3D Photogrammetry3D printer360x3D scene capture3-D SHAPE RECOVERY3d mapping360 Video3A ALGORITHMS3D-high efficiency video coding3D visual representation3D DISPLAY3D USER INTERFACES3D TV3D PROFILE360-Degree Video Technology3D cinema and TV3D Reconstruction360-degree imaging3D CG Image3D Color Printing3D-printing3D Range Data Encoding3d3d video3A Algorithms3D STIMULI3D IMAGE3D vision3DGS3D3D encoding3D glasses3D COMPRESSION AND ENCRYPTION3D surface structure based halftoning
Images posted online present a privacy concern in that they may be used as reference examples for a facial recognition system. Such abuse of images is in violation of privacy rights but is difficult to counter. It is well established that adversarial example images can be created for recognition systems which are based on deep neural networks. These adversarial examples can be used to disrupt the utility of the images as reference examples or training data. In this work we use a Generative Adversarial Network (GAN) to create adversarial examples to deceive facial recognition and we achieve an acceptable success rate in fooling the face recognition. Our results reduce the training time for the GAN by removing the discriminator component. Furthermore, our results show knowledge distillation can be employed to drastically reduce the size of the resulting model without impacting performance indicating that our contribution could run comfortably on a smartphone.