Abstract: Aspects of the disclosure relate to mitigation and detection of steganographic modifications embedded in images. A computing platform may receive an image embedded with steganographic modifications. The computing platform may change or modify any number of bits of one or more color components of one or more pixels of an image, rendering the steganographic modifications ineffective. The computing platform may cause at an isolation zone system, execution of an image, including steganographic modifications, to identify images embedded with steganographic modifications. The computing platform may also compare an image with image stored in an image storage module. The computing platform may store an image from the image storage module with a highest visual comparison score rather than the image.

Abstract: Aspects of the disclosure relate to mitigation and detection of steganographic modifications embedded in images. A computing platform may receive an image embedded with steganographic modifications. The computing platform may change or modify any number of bits of one or more color components of one or more pixels of an image, rendering the steganographic modifications ineffective. The computing platform may cause at an isolation zone system, execution of an image, including steganographic modifications, to identify images embedded with steganographic modifications. The computing platform may also compare an image with image stored in an image storage module. The computing platform may store an image from the image storage module with a highest visual comparison score rather than the image.

Abstract: Systems, devices, and methods for protecting a user computer devices/network from malicious code embedded in a neural network is described. A security platform may selectively modify a downloaded neural network model and/or architecture to remove neural network parameters that may be used to reconstruct the malicious code at an end user of the neural network model. For example, the security platform may remove specific branches of the neural network and/or set specific parameters of the neural network model to zero, such that the malicious code may not be reconstructed at an end-user device.

Abstract: Systems, devices, and methods for protecting a user computer devices/network from malicious code embedded in a neural network is described. A security platform may selectively modify a downloaded neural network model and/or architecture to remove neural network parameters that may be used to reconstruct the malicious code at an end user of the neural network model. For example, the security platform may remove specific branches of the neural network and/or set specific parameters of the neural network model to zero, such that the malicious code may not be reconstructed at an end-user device.

Abstract: Aspects of the disclosure relate to mitigation and detection of steganographic modifications embedded in images. A computing platform may receive an image embedded with steganographic modifications. The computing platform may change or modify any number of bits of one or more color components of one or more pixels of an image, rendering the steganographic modifications ineffective. The computing platform may cause at an isolation zone system, execution of an image, including steganographic modifications, to identify images embedded with steganographic modifications. The computing platform may also compare an image with image stored in an image storage module. The computing platform may store an image from the image storage module with a highest visual comparison score rather than the image.

Abstract: A method for optimizing an application of an evolutionary-strategy-based application of Artificial Intelligence (AI) is provided. The application may be performed on a pre-determined surface. The method may include selecting a first group of candidates, determining a mean and standard deviation associated with the first group of candidates, using a static scaling factor to formulate a size of a population of candidates for generation, using the mean and the standard deviation to generate, according to the size, the population of candidates, and selecting a second group of candidates from among the population of candidates. Each member of the second group of candidates is closer to a minimum value of the surface than a remainder of the population of candidates. The remainder of the population of candidates may be formed from a group of non-selected candidates among the population of candidates.