Robert Boschgmbh

Filipe Condessa

Chaithanya Kumar Mummadi

Aniruddha Saha

Max Welling

Wan-Yi Lin

Devin T Willmott

Jeremy Zieg Kolter

João D Semedo

Wan-yi Lin

Maja Rudolph

Shaojie Bai

Shuhua Yu

Yash Savani

Sindy Lowe

A computer-implemented system and method relate to certified robust defenses against adversarial patches. A set of one-mask images are generated using a source image and a first mask at a set of predetermined image regions. The set of predetermined image regions collectively cover at least every pixel of the source image. A particular one-mask image with a highest prediction loss is selected from among the set of one-mask images. A set of two-mask images is generated using the selected one-mask image and a second mask at the set of predetermined image regions. A particular two-mask image with a highest prediction loss is selected from among the set of two-mask images. The machine learning system is trained using a training dataset, which includes the selected two-mask image.

System and method with masking for certified defenses against adversarial patches

A system and method include dividing a source image into a plurality of source regions, which are portions of the source image that correspond to a plurality of grid regions. A mask is used to create a first masked region that masks a first source region and a first unmasked region that comprises a second source region. A first inpainted region is generated by inpainting the first masked region based on the second source region. Similarity data is generated based on a similarity assessment. A protected image is generated that includes at least (i) the first masked region at a first grid region when the similarity data indicates that the first source region is not similar to the first inpainted region and (ii) the first inpainted region at the first grid region when the similarity data indicates that the first source region is similar to the first inpainted region.

System and method with masking and inpainting strategy for generic defense against patch attacks

A computer-implemented method for a machine learning (ML) system includes receiving a first image frame and a second frame from a sensor, wherein the first and second image frames are time series data, determining a first flow state and a first latent state of the first image frame, determining a Deep Equilibrium Model (DEQ) based fix point solution via a root finding method based on the first flow state, the first latent state, and a layer function to obtain an estimated flow and latent state, receiving a third image frame, wherein the second and third image frames are time series data, determining the fix point solution via the root finding method based on the estimated flow state, the estimated latent state, and layer function to obtain an updated flow state and updated latent state, and outputting the updated flow state.

Deep equilibrium flow estimation

A computer-implemented system and method relate to object discovery. The system and method include receiving a source image and generating input data by associating each pixel of the source image with predetermined phase values. An encoder encodes the input data to generate latent representation data in spherical coordinates. A decoder decodes the latent representation data to generate spherical reconstruction data of the source image. The spherical reconstruction data includes a radial component and a plurality of phase components. A reconstructed image is generated based at least on the radial component. The reconstructed image is a reconstruction of the source image.

System and method with spherical autoencoder for object discovery

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Pittsburgh, PA, United States of America

Filipe Condessa