A computer-implemented method for training a data embedding network capable of integrating original data with mark data to thereby generate marked data, the computer-implemented method for training comprising: (a) integrating, using the data embedding network, original training data with mark training data, thereby generating marked training data (X'); (b) (i) applying, using a learning network having its own one or more learned parameters, a network operation to the marked training data (X') using the learned parameters, thereby generating 1-st characteristic information corresponding to the marked training data, and (ii) applying, using the learning network, a network operation to the original training data using the learned parameters, thereby generating 2-nd characteristic information corresponding to the original training data; and (c) training the data embedding network such that: (i) a data error is minimized which is acquired by referring to (i-1) a 1-st error acquired by referring to the 1-st characteristic information and the 2-nd characteristic information, and (i-2) a 2-nd error acquired by referring to a task specific output generated by using the 1-st characteristic information and a ground truth corresponding to the task specific output, and (ii) a marked data score is maximized which corresponds to the marked training data inputted into a discriminator for determining whether inputted data is real or fake, and training the discriminator such that: a modified data score or a modified marked data score is maximized which respectively corresponds to modified training data or modified marked training data inputted into the discriminator, wherein the modified training data or the modified marked training data is generated respectively by modifying the original training data or the marked training data, and the marked data score is minimized. (a) integrating, using the data embedding network, original training data with mark training data, thereby generating marked training data (X'); (b) (i) applying, using a learning network having its own one or more learned parameters, a network operation to the marked training data (X') using the learned parameters, thereby generating 1-st characteristic information corresponding to the marked training data, and (ii) applying, using the learning network, a network operation to the original training data using the learned parameters, thereby generating 2-nd characteristic information corresponding to the original training data; and (c) training the data embedding network such that: (i) a data error is minimized which is acquired by referring to (i-1) a 1-st error acquired by referring to the 1-st characteristic information and the 2-nd characteristic information, and (i-2) a 2-nd error acquired by referring to a task specific output generated by using the 1-st characteristic information and a ground truth corresponding to the task specific output, and (ii) a marked data score is maximized which corresponds to the marked training data inputted into a discriminator for determining whether inputted data is real or fake, and (i) a data error is minimized which is acquired by referring to (i-1) a 1-st error acquired by referring to the 1-st characteristic information and the 2-nd characteristic information, and (i-2) a 2-nd error acquired by referring to a task specific output generated by using the 1-st characteristic information and a ground truth corresponding to the task specific output, and (ii) a marked data score is maximized which corresponds to the marked training data inputted into a discriminator for determining whether inputted data is real or fake, and training the discriminator such that: a modified data score or a modified marked data score is maximized which respectively corresponds to modified training data or modified marked training data inputted into the discriminator, wherein the modified training data or the modified marked training data is generated respectively by modifying the original training data or the marked training data, and the marked data score is minimized. a modified data score or a modified marked data score is maximized which respectively corresponds to modified training data or modified marked training data inputted into the discriminator, wherein the modified training data or the modified marked training data is generated respectively by modifying the original training data or the marked training data, and the marked data score is minimized.