Methods, computer devices, and non-transitory computer-readable record media for learning of watermarking model using complex attack
Disclosed are a method, a computer device, and a non-transitory computer-readable record medium for learning of a watermarking model. A watermarking model learning method may include dividing, by the at least one processor, epochs for learning of a watermarking model into at least two stages, setting, by the at least one processor, at least one target attack type to each stage among the at least two stages, the setting includes setting a first threshold number of the at least one target attack type set to an earlier stage to be greater than a second threshold number of the at least one target attack type set to a later stage, and the at least two stages including the earlier stage and the later stage, and performing, by the at least one processor, learning of the watermarking model based on the at least two stages and the setting.
1 . A watermarking model learning method executed by a computer device, the computer device including at least one processor configured to execute computer-readable instructions stored in a memory, and the watermarking model learning method comprises:
dividing, by the at least one processor, epochs for learning of a watermarking model into at least two stages;
setting, by the at least one processor, at least one target attack type to each stage among the at least two stages, the setting includes setting a first threshold number of the at least one target attack type set to an earlier stage to be greater than a second threshold number of the at least one target attack type set to a later stage, and the at least two stages including the earlier stage and the later stage; and
performing, by the at least one processor, learning of the watermarking model based on the at least two stages and the setting,
wherein
model learning method further comprises configuring, by the at least one processor, a respective learning batch corresponding to the at least one target attack type set to each corresponding stage among the at least two stages,
the configuring comprises configuring the respective learning batch to include an original sample and an attack sample, the configuring being based on an original retention probability and an attack application probability,
the original sample is a learning sample to which a first target attack type among the at least one target attack type is not applied, and
the attack sample is a learning sample to which the first target attack type is applied.
2 . The watermarking model learning method of claim 1 , wherein
the first threshold number is one;
the earlier stage is an initial stage in which the learning starts; and
the second threshold number is at least two.
3 . The watermarking model learning method of claim 1 , wherein the setting comprises randomly selecting the at least one target attack type from an attack type list according to a respective threshold number of the at least one target attack type of each corresponding stage among the at least two stages.
4 . The watermarking model learning method of claim 1 , wherein the configuring comprises:
selecting learning samples through random sampling from a dataset, the learning samples including the original sample and the attack sample; and
generating the attack sample by applying the first target attack type to at least one of the learning samples.
5 . The watermarking model learning method of claim 1 , wherein the configuring comprises randomly setting target parameters within a parameter range of the first target attack type as learning samples to which the first target attack type is applied to obtain the respective learning batch, the learning samples including the attack sample.
6 . The watermarking model learning method of claim 1 , wherein the configuring comprises configuring the respective learning batch by generating a learning sample to which the at least one target attack type set to the later stage is applied.
7 . The watermarking model learning method of claim 1 , wherein the configuring comprises configuring the respective learning batch by generating a plurality of learning samples to which at least two target attack types are applied, at least one of a combination of the at least two target attack types or an application order of the at least two target attack types being different among the plurality of learning samples.
8 . The watermarking model learning method of claim 1 , wherein the configuring comprises configuring the respective learning batch by replacing a non-differentiable attack type with a differentiable attack type based on an approximation function, the non-differentiable attack type being among the at least one target attack type.
9 . A non-transitory computer-readable recording medium storing program instructions that, when executed by at least one processor, cause the at least one processor to perform the watermarking model learning method of claim 1 .
10 . A computer device comprising:
at least one processor configured to execute computer-readable instructions included in a memory to cause the computer device to
divide epochs for learning of a watermarking model into at least two stages,
set at least one target attack type to each stage among the at least two stages,
set a first threshold number of the at least one target attack type set to an earlier stage to be greater than a second threshold number of the at least one target attack type set to a later stage, and the at least two stages including the earlier stage and the later stage, and
perform learning of the watermarking model based on the at least two stages, the at least one target attack type set to each stage among the at least two stages, the first threshold number and the second threshold number,
wherein
the at least one processor is configured to cause the computer device to configure a respective learning batch corresponding to the at least one target attack type set to each corresponding stage among the at least two stages,
the at least one processor is configured to cause the computer device to configure the respective learning batch to include an original sample and an attack sample based on an original retention probability and an attack application probability,
the original sample is a learning sample to which a first target attack type among the at least one target attack type is not applied, and
the attack sample is a learning sample to which the first target attack type is applied.
11 . The computer device of claim 10 , wherein
the first threshold number is one;
the earlier stage is an initial stage in which the learning starts; and
the second threshold number is at least two.
12 . The computer device of claim 10 , wherein the at least one processor is configured to cause the computer device to randomly select the at least one target attack type from an attack type list according to a respective threshold number of the at least one target attack type of each corresponding stage among the at least two stages.
13 . The computer device of claim 10 , wherein the at least one processor is configured to cause the computer device to:
select learning samples through random sampling from a dataset, the learning samples including the original sample and the attack sample; and
generate the attack sample by applying the first target attack type to at least one of the learning samples.
14 . The computer device of claim 10 , wherein the at least one processor is configured to cause the computer device to randomly set target parameters within a parameter range of the first target attack type as learning samples to which the first target attack type to obtain the respective learning batch, the learning samples including the attack sample.
15 . The computer device of claim 10 , wherein the at least one processor is configured to cause the computer device to configure the respective learning batch by generating a learning sample to which the at least one target attack type set to the later stage is applied.
16 . The computer device of claim 10 , wherein the at least one processor is configured to cause the computer device to configure the respective learning batch by generating a plurality of learning samples to which at least two target attack types are applied, at least one of a combination of the at least two target attack types or an application order of the at least two target attack types being different among the plurality of learning samples.