US 12,456,283 B2
Identifying visual contents used for training of inference models
Yair Adato, Kfar Ben Nun (IL); Ran Achituv, Kefar Sava (IL); Eyal Gutflaish, Beer Sheva (IL); and Dvir Yerushalmi, Kfar Saba (IL)
Assigned to BRIA ARTIFICIAL INTELLIGENCE LTD, Tel Aviv (IL)
Filed by BRIA ARTIFICIAL INTELLIGENCE LTD, Tel Aviv (IL)
Filed on Nov. 14, 2022, as Appl. No. 17/986,378.
Application 17/986,378 is a continuation of application No. PCT/IL2022/051189, filed on Nov. 9, 2022.
Claims priority of provisional application 63/279,111, filed on Nov. 14, 2021.
Prior Publication US 2023/0154153 A1, May 18, 2023
Int. Cl. G06V 10/764 (2022.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06T 3/40 (2024.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06T 19/00 (2011.01); G06V 10/772 (2022.01); G06V 10/774 (2022.01)
CPC G06V 10/764 (2022.01) [G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06T 3/40 (2013.01); G06T 5/50 (2013.01); G06T 7/0002 (2013.01); G06T 11/001 (2013.01); G06T 19/006 (2013.01); G06V 10/772 (2022.01); G06V 10/774 (2022.01); G06T 2207/20081 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A non-transitory computer readable medium containing instructions for causing at least one processor to perform operations for identifying visual contents used for training of inference models, the operations comprising:
receiving a specific visual content;
accessing data based on at least one parameter of an inference model, the inference model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples includes a visual content;
analyzing the data and the specific visual content to determine a likelihood that the specific visual content is included in at least one training example of the plurality of training examples;
generating a digital signal indicative of the likelihood that the specific visual content is included in at least one training example of the plurality of training examples;
calculating a convolution of at least part of the specific visual content to thereby obtain a result value of the calculated convolution of the at least part of the specific visual content;
calculating a mathematical function of the result value of the calculated convolution of the at least part of the specific visual content;
selecting a threshold based on the data;
comparing the threshold with the mathematical function of the result value of the calculated convolution of the at least part of the specific visual content; and
determining the likelihood that the specific visual content is included in at least one training example of the plurality of training examples based on a result of the comparison of the threshold with the mathematical function of the result value of the calculated convolution of the at least part of the specific visual content.