For the rest, we quantify the tradeoff between privacy and utility and demonstrate that utility remains acceptable even with strong privacy protection. Go through all image tensors one by one and computing their MSE. ![]() For instance, the main subject of the image (here, the alarm clock) will never be cropped in its middle. As you can see, the modification is not intended to be destructive, but rather ameliorative. In most cases, privacy enforcement does not reduce the applications’ functionality or accuracy. Matching a pair of any of the images above should result in finding a duplicate. These applications run on DARKLY unmodified or with very few modifications and minimal performance overheads vs. We evaluate DARKLY on 20 perceptual applications that perform diverse tasks such as image recognition, object tracking, security surveillance, and face detection. It deploys multiple privacy protection mechanisms, including access control, algorithmic privacy transforms, and user audit. To start the scanner with a video file, run this command: python3 scanner.py -v cup1.mp4 You can omit the -v option if you dont want to see debug output. DARKLY is integrated with OpenCV, a popular computer vision library used by such applications to access visual inputs. We describe the design and implementation of DARKLY, a practical privacy protection system for the increasingly common scenario where an untrusted, third-party perceptual application is running on a trusted device. Perceptual, “context-aware” applications that observe their environment and interact with users via cameras and other sensors are becoming ubiquitous on personal computers, mobile phones, gaming platforms, household robots, and augmented-reality devices.
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