DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims: claims 1-20 are pending below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6, 8, 9-11, 13, 15-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al (US 12,300,035) in view of MA (US 2020/0372272).
Claim 1, similar claims 9 and 15:
Xu et al (US 12,300,035) teaches the following subject matter:
A method for detecting an injection attack, the method comprising:
obtaining a video that includes a plurality of video frames of a target face that is illuminated according to a plurality of light values (column 3 lines 15-35 detail set of images (video) with color light (plurality of light values) from display to reflected from face are captured for analysis to determine if face is genuine or not; column 4 lines 30-62);
performing a grayscale transformation on a video frame of the video to obtain a first grayscale value of the video frame (column 5 line 50-column 6 line 10 detail digital image with RBG to be greyscale);
determining whether the video is subjected to the injection attack based on the first grayscale value and the second grayscale value (column 5 line 50-column 6 line 10 the use of colors and grayscale to different frames for detection of injection attach (deep fake); column 9 line 63-column 10 line 10 teaches further details, where column 16 line 15-43 detail if the difference is significant greater then baseline use to determine injection attack).
Xu et al teaches all the subject matter regarding greyscale for different frames (column 5 line 50-column 6 line 10 detail digital image to different greyscale for each pixel in different frames: I-frame, P-frame, B-frame) above, but not the following:
converting a light value of the plurality of light values to obtain a second grayscale value corresponding to a light attribute applied in the video frame.
MA (US 2020/0372272) teaches the following subject matter: converting a light value of the plurality of light values to obtain a second grayscale value corresponding to a light attribute applied in the video frame (0030-0035, specifically 0034 detail difference between first and second image which in this case pixel values are grayscale value for reach; 0050 also detail first grayscale and second grayscale dependent on position of bright spot).
Xu et al and MA are both in the field of image analysis, especially detecting injection attach (deepfake) by using grayscale on region of interest such as the face such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Xu et al by MA using grayscale with light sources/difference where such fluctuation degree of the position of the bright spot is large enough (i.e., the first confidence is small enough), it is determined that the object to be detected may be the non-living object, thereby recognizing the non-living object more likely, and further imrpove the detection accuracy as disclosed by MA in paragraph 0058.
Claim 2, similar claims 10 and 16:
Xu et al teach:
The method according to claim 1, wherein the performing the grayscale transformation further comprises:
determining a key facial area in the video frame that satisfies a preset pixel distribution condition based on pixel values in the video frame (column 17 line 55-65 detail face landmarks that are extracted and compared to color, luminance or luminance gradient information about color light patterns; column 17 lin3 65 column 18 line 10 detail key facial area such as eyes, eyeglasses, mustaches, beard); performing the grayscale transformation on the key facial area to obtain a first grayscale image (above teaches color and grayscale); and obtaining the first grayscale value based on a mean value of the first grayscale image (column 18 line 50-56 detail the use of mean, average and statistical combination for these facial landmark).
Claim 4, similar claims 11 and 17:
Xu et al teach:
The method according to claim 1, wherein the determining whether the video is subjected to the injection attack further comprises:
generating a first grayscale value sequence of the video that includes the first grayscale value; generating a second grayscale value sequence of the video that includes the second grayscale value; performing a correlation analysis on the first grayscale value sequence and the second grayscale value sequence to obtain a first coefficient (column 19 line 58 to column 20 line 5 detail computing correlation coefficient); and when the first coefficient is less than a preset coefficient, determining the video is subjected to the injection attack (column 19 line 58 to column 20 line 5 detail using coefficient to determine attack).
Claim 6, similar claims 13 and 19:
Xu et al teach:
The method according claim 1, further comprising: obtaining a sequence of target RGB values of a light that illuminates the target face; and controlling the light to illuminate the target face based on the sequence of target RGB values (column 5 lines 50-65 detail image to be RGB due to plurality of light on the face as detail above.).
Claim 8:
Xu et al teach:
The method according to claim 6, further comprising: obtaining a non-color attribute sequence of at least one of brightness values or exposure times; and based on the sequence of target RGB values and the non-color attribute sequence, controlling the light to illuminate the target face (column 5 lines 50-65 detail image to be RGB, greyscale, light intensity (brightness values) and non-color such as : I-frame, P-frame, B-frame).
Allowable Subject Matter
Claim 3 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. At the time of examination unable to find prior art solo or in combination regarding “based on a light illumination direction on the target face, determining an imaging shadow area of the target face in the video frame; performing the grayscale transformation on the imaging shadow area to obtain a second grayscale image; and obtaining the first grayscale value based on a mean value of the second grayscale image.”
Claim 5, similar claims 12 and 18, are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. At the time of examination unable to find prior art solo or in combination regarding “obtaining an interval duration between an acquisition time of the video and a generation time of a light that illuminates the target face; based on the interval duration, aligning the first grayscale value sequence and the second grayscale value sequence; and performing the correlation analysis on the first grayscale value sequence and the second grayscale value sequence based on the aligned first grayscale value sequence and second grayscale value sequence to obtain the first coefficient.”
Claim 7, similar claims 14 and 20, are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. At the time of examination unable to find prior art solo or in combination regarding “selecting a maximum RGB value and a minimum RGB value, and selecting at least one RGB value in between the maximum RGB value and the minimum RGB value, the R, the G, and the B components being equal values; and obtaining the sequence of target RGB values based on a combination of the maximum RGB value, the minimum RGB value, and the at least one RGB value.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nießner et al (US 10,964,006) teaches Identification Of Neural-network-generated Fake Images - fake video may include a noise property of a shadow region 412 located proximate to and at a former position of a moving object 414 in the sequence of images 410 in video 400. For example, the noise property may include a speckled pattern of greyscale values in the shadow region 412 in a given image. Moreover, after object 414 has passed, the noise at a given location in a given image may decrease as a function of time in the sequence of images 410. In some embodiments, the noise property may include root-mean-square noise, and/or may correspond to one or more moments of a distribution. Note that the noise property may be determined using a histogram of intensity values in the shadow region 412 relative to a histogram of intensity values in one or more other regions in the given image (such as a region that is not located proximate to or at a former position of a moving object 414 in the sequence of images 410) (column 10 line 55-68).
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
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