STEGANOGRAPHIC MODIFICATION DETECTION AND MITIGATION FOR ENHANCED ENTERPRISE SECURITY

    公开(公告)号:US20250013749A1

    公开(公告)日:2025-01-09

    申请号:US18895529

    申请日:2024-09-25

    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.

    Steganographic modification detection and mitigation for enhanced enterprise security

    公开(公告)号:US12147538B2

    公开(公告)日:2024-11-19

    申请号:US17872354

    申请日:2022-07-25

    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.

    Data feed meta detail categorization for confidence

    公开(公告)号:US12050587B2

    公开(公告)日:2024-07-30

    申请号:US17334659

    申请日:2021-05-28

    CPC classification number: G06F16/2365 G06F11/0784 G06F16/285 G06F11/0775

    Abstract: Aspects of the disclosure relate to data feed meta detail categorization for confidence. A computing platform may retrieve source data from a source system and identify a first set of patterns associated with the source data. The computing platform may retrieve, from a target system, transferred data associated with a data transfer from the source system to the target system and identify a second set of patterns associated with transferred data. The computing platform may evaluate integrity of the transferred data by comparing the first set of patterns with the second set of patterns. The computing platform may detect whether the first set of patterns falls within an expected deviation from the second set of patterns based on the comparison. The computing platform may send one or more notifications based on detecting that the first set of patterns falls outside the expected deviation from the second set of patterns.

    Data feed meta detail categorization for confidence

    公开(公告)号:US12056112B2

    公开(公告)日:2024-08-06

    申请号:US17334646

    申请日:2021-05-28

    CPC classification number: G06F16/2365 G06F16/215 G06F16/285 G06N20/00

    Abstract: Aspects of the disclosure relate to data feed meta detail categorization for confidence. A computing platform may retrieve source data from a source system and identify a first set of patterns associated with the source data. The computing platform may retrieve, from a target system, partially transferred data associated with an ongoing data transfer from the source to the target system and identify a second set of patterns associated with the partially transferred data. The computing platform may evaluate integrity of the partially transferred data by comparing the first set of patterns with the second set of patterns. The computing platform may detect whether the first set of patterns falls within an expected deviation from the second set of patterns based on the comparison and halt the ongoing data transfer based on detecting that the first set of patterns falls outside the expected deviation from the second set of patterns.

    STEGANOGRAPHIC MODIFICATION DETECTION AND MITIGATION FOR ENHANCED ENTERPRISE SECURITY

    公开(公告)号:US20240028727A1

    公开(公告)日:2024-01-25

    申请号:US17872354

    申请日:2022-07-25

    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.

    Data Feed Meta Detail Categorization for Confidence

    公开(公告)号:US20220382737A1

    公开(公告)日:2022-12-01

    申请号:US17334646

    申请日:2021-05-28

    Abstract: Aspects of the disclosure relate to data feed meta detail categorization for confidence. A computing platform may retrieve source data from a source system and identify a first set of patterns associated with the source data. The computing platform may retrieve, from a target system, partially transferred data associated with an ongoing data transfer from the source to the target system and identify a second set of patterns associated with the partially transferred data. The computing platform may evaluate integrity of the partially transferred data by comparing the first set of patterns with the second set of patterns. The computing platform may detect whether the first set of patterns falls within an expected deviation from the second set of patterns based on the comparison and halt the ongoing data transfer based on detecting that the first set of patterns falls outside the expected deviation from the second set of patterns.

    Data Feed Meta Detail Categorization for Confidence

    公开(公告)号:US20220382618A1

    公开(公告)日:2022-12-01

    申请号:US17334659

    申请日:2021-05-28

    Abstract: Aspects of the disclosure relate to data feed meta detail categorization for confidence. A computing platform may retrieve source data from a source system and identify a first set of patterns associated with the source data. The computing platform may retrieve, from a target system, transferred data associated with a data transfer from the source system to the target system and identify a second set of patterns associated with transferred data. The computing platform may evaluate integrity of the transferred data by comparing the first set of patterns with the second set of patterns. The computing platform may detect whether the first set of patterns falls within an expected deviation from the second set of patterns based on the comparison. The computing platform may send one or more notifications based on detecting that the first set of patterns falls outside the expected deviation from the second set of patterns.

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