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• Why JPEG image looks different in two videos ?

  28 septembre 2015, par user606521

  I have two frames :

  f1.jpg
  
  f2.jpg
  

  And I am creating two videos :

  ./ffmpeg -i ./f%d.jpg -r 30 -y m1.mp4 # both frames 1 and 2
  
  ./ffmpeg -i ./f2.jpg -r 30 -y m2.mp4 # only frame 2

  For some reason frame 2 looks different in these two videos

  Frame 2 from m1.mp4 :

  $ ./ffmpeg -i m1.mp4 out1_%d.jpg
  
  ffmpeg version 2.7.2 Copyright (c) 2000-2015 the FFmpeg developers
  
    built with llvm-gcc 4.2.1 (LLVM build 2336.11.00)
  
    configuration: --prefix=/Volumes/Ramdisk/sw --enable-gpl --enable-pthreads --enable-version3 --enable-libspeex --enable-libvpx --disable-decoder=libvpx --enable-libmp3lame --enable-libtheora --enable-libvorbis --enable-libx264 --enable-avfilter --enable-libopencore_amrwb --enable-libopencore_amrnb --enable-filters --enable-libgsm --enable-libvidstab --enable-libx265 --disable-doc --arch=x86_64 --enable-runtime-cpudetect
  
    libavutil      54. 27.100 / 54. 27.100
  
    libavcodec     56. 41.100 / 56. 41.100
  
    libavformat    56. 36.100 / 56. 36.100
  
    libavdevice    56.  4.100 / 56.  4.100
  
    libavfilter     5. 16.101 /  5. 16.101
  
    libswscale      3.  1.101 /  3.  1.101
  
    libswresample   1.  2.100 /  1.  2.100
  
    libpostproc    53.  3.100 / 53.  3.100
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'm1.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.36.100
  
    Duration: 00:00:00.07, start: 0.000000, bitrate: 478 kb/s
  
      Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 980x100, 381 kb/s, 30 fps, 30 tbr, 15360 tbn, 60 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
  [swscaler @ 0x7f7f9a000600] deprecated pixel format used, make sure you did set range correctly
  
  Output #0, image2, to 'out1_%d.jpg':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.36.100
  
      Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 980x100, q=2-31, 200 kb/s, 30 fps, 30 tbn, 30 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
        encoder         : Lavc56.41.100 mjpeg
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))
  
  Press [q] to stop, [?] for help
  
  frame=    2 fps=0.0 q=1.6 Lsize=N/A time=00:00:00.06 bitrate=N/A    
  
  video:6kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown

  

  Frame 2 from m2.mp4 :

  $ ./ffmpeg -i m2.mp4 out2_%d.jpg
  
  ffmpeg version 2.7.2 Copyright (c) 2000-2015 the FFmpeg developers
  
    built with llvm-gcc 4.2.1 (LLVM build 2336.11.00)
  
    configuration: --prefix=/Volumes/Ramdisk/sw --enable-gpl --enable-pthreads --enable-version3 --enable-libspeex --enable-libvpx --disable-decoder=libvpx --enable-libmp3lame --enable-libtheora --enable-libvorbis --enable-libx264 --enable-avfilter --enable-libopencore_amrwb --enable-libopencore_amrnb --enable-filters --enable-libgsm --enable-libvidstab --enable-libx265 --disable-doc --arch=x86_64 --enable-runtime-cpudetect
  
    libavutil      54. 27.100 / 54. 27.100
  
    libavcodec     56. 41.100 / 56. 41.100
  
    libavformat    56. 36.100 / 56. 36.100
  
    libavdevice    56.  4.100 / 56.  4.100
  
    libavfilter     5. 16.101 /  5. 16.101
  
    libswscale      3.  1.101 /  3.  1.101
  
    libswresample   1.  2.100 /  1.  2.100
  
    libpostproc    53.  3.100 / 53.  3.100
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'm2.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.36.100
  
    Duration: 00:00:00.03, start: 0.000000, bitrate: 824 kb/s
  
      Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuvj420p(pc), 980x100 [SAR 1:1 DAR 49:5], 648 kb/s, 30 fps, 30 tbr, 15360 tbn, 60 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
  Output #0, image2, to 'out2_%d.jpg':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf56.36.100
  
      Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 980x100 [SAR 1:1 DAR 49:5], q=2-31, 200 kb/s, 30 fps, 30 tbn, 30 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler
  
        encoder         : Lavc56.41.100 mjpeg
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))
  
  Press [q] to stop, [?] for help
  
  frame=    1 fps=0.0 q=1.6 Lsize=N/A time=00:00:00.03 bitrate=N/A    
  
  video:4kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown

  

  It seems that frame 2 looks different when combined with frame 1 in a video. One thing I noticed that when dumping frames from m1.mp4 (two frames combined) there is a warning in ffmpeg output :

  [swscaler @ 0x7f7f9a000600] deprecated pixel format used, make sure you did set range correctly

  Also two frame images have same dimmensions (980x100) and their format seems to be same :

  $ file -b f1.jpg
  
  JPEG image data, JFIF standard 1.01, comment: "CS=ITU601??"
  
  $ file -b f2.jpg 
  
  JPEG image data, JFIF standard 1.01

  So why this is happening ? I am struggling with this issue for quite a long time - for some reason some JPEGs somehow are in different "format ?" than others and make mess in videos...

  ---

  You can download first two images included in this question and test commands on your own.

  ---

  $ identify -verbose f1.jpg
  
  Image: f1.jpg
  
    Format: JPEG (Joint Photographic Experts Group JFIF format)
  
    Mime type: image/jpeg
  
    Class: DirectClass
  
    Geometry: 980x100+0+0
  
    Resolution: 552x551
  
    Print size: 1.77536x0.181488
  
    Units: PixelsPerInch
  
    Type: Palette
  
    Endianess: Undefined
  
    Colorspace: sRGB
  
    Depth: 8-bit
  
    Channel depth:
  
      red: 8-bit
  
      green: 8-bit
  
      blue: 8-bit
  
    Channel statistics:
  
      Red:
  
        min: 189 (0.741176)
  
        max: 233 (0.913725)
  
        mean: 208.261 (0.81671)
  
        standard deviation: 14.1741 (0.0555848)
  
        kurtosis: -1.40078
  
        skewness: 0.332664
  
      Green:
  
        min: 189 (0.741176)
  
        max: 233 (0.913725)
  
        mean: 208.261 (0.81671)
  
        standard deviation: 14.1741 (0.0555848)
  
        kurtosis: -1.40078
  
        skewness: 0.332664
  
      Blue:
  
        min: 189 (0.741176)
  
        max: 233 (0.913725)
  
        mean: 208.26 (0.816706)
  
        standard deviation: 14.1756 (0.0555905)
  
        kurtosis: -1.40068
  
        skewness: 0.332424
  
    Image statistics:
  
      Overall:
  
        min: 189 (0.741176)
  
        max: 233 (0.913725)
  
        mean: 208.261 (0.816709)
  
        standard deviation: 14.1746 (0.0555867)
  
        kurtosis: -1.40075
  
        skewness: 0.332584
  
    Colors: 47
  
    Histogram:
  
         368: (189,189,189) #BDBDBD grey74
  
        1984: (190,190,190) #BEBEBE grey
  
          28: (191,191,189) #BFBFBD srgb(191,191,189)
  
        3780: (191,191,191) #BFBFBF grey75
  
          28: (192,192,190) #C0C0BE srgb(192,192,190)
  
        6628: (192,192,192) #C0C0C0 silver
  
        5664: (193,193,193) #C1C1C1 srgb(193,193,193)
  
        4608: (194,194,194) #C2C2C2 grey76
  
        4480: (195,195,195) #C3C3C3 srgb(195,195,195)
  
        3328: (196,196,196) #C4C4C4 grey77
  
        2592: (197,197,197) #C5C5C5 srgb(197,197,197)
  
        3072: (198,198,198) #C6C6C6 srgb(198,198,198)
  
        2272: (199,199,199) #C7C7C7 grey78
  
        2112: (200,200,200) #C8C8C8 srgb(200,200,200)
  
        2112: (201,201,201) #C9C9C9 grey79
  
        1920: (202,202,202) #CACACA srgb(202,202,202)
  
        1728: (203,203,203) #CBCBCB srgb(203,203,203)
  
        1760: (204,204,204) #CCCCCC grey80
  
        1696: (205,205,205) #CDCDCD srgb(205,205,205)
  
        1248: (206,206,206) #CECECE srgb(206,206,206)
  
        1536: (207,207,207) #CFCFCF grey81
  
        1504: (208,208,208) #D0D0D0 srgb(208,208,208)
  
        1344: (209,209,209) #D1D1D1 grey82
  
        1536: (210,210,210) #D2D2D2 srgb(210,210,210)
  
        1472: (211,211,211) #D3D3D3 LightGray
  
        1088: (212,212,212) #D4D4D4 grey83
  
        1472: (213,213,213) #D5D5D5 srgb(213,213,213)
  
        1536: (214,214,214) #D6D6D6 grey84
  
        1344: (215,215,215) #D7D7D7 srgb(215,215,215)
  
        1184: (216,216,216) #D8D8D8 srgb(216,216,216)
  
        1408: (217,217,217) #D9D9D9 grey85
  
        1472: (218,218,218) #DADADA srgb(218,218,218)
  
        1216: (219,219,219) #DBDBDB grey86
  
        1280: (220,220,220) #DCDCDC gainsboro
  
        1536: (221,221,221) #DDDDDD srgb(221,221,221)
  
        1472: (222,222,222) #DEDEDE grey87
  
        1600: (223,223,223) #DFDFDF srgb(223,223,223)
  
        1696: (224,224,224) #E0E0E0 grey88
  
        1792: (225,225,225) #E1E1E1 srgb(225,225,225)
  
        1728: (226,226,226) #E2E2E2 srgb(226,226,226)
  
        1952: (227,227,227) #E3E3E3 grey89
  
        2272: (228,228,228) #E4E4E4 srgb(228,228,228)
  
        2752: (229,229,229) #E5E5E5 grey90
  
        4512: (230,230,230) #E6E6E6 srgb(230,230,230)
  
        4672: (231,231,231) #E7E7E7 srgb(231,231,231)
  
         640: (232,232,232) #E8E8E8 grey91
  
         576: (233,233,233) #E9E9E9 srgb(233,233,233)
  
    Rendering intent: Perceptual
  
    Gamma: 0.454545
  
    Chromaticity:
  
      red primary: (0.64,0.33)
  
      green primary: (0.3,0.6)
  
      blue primary: (0.15,0.06)
  
      white point: (0.3127,0.329)
  
    Background color: white
  
    Border color: srgb(223,223,223)
  
    Matte color: grey74
  
    Transparent color: black
  
    Interlace: None
  
    Intensity: Undefined
  
    Compose: Over
  
    Page geometry: 980x100+0+0
  
    Dispose: Undefined
  
    Iterations: 0
  
    Compression: JPEG
  
    Orientation: Undefined
  
    Properties:
  
      comment: CS=ITU601
  
      date:create: 2015-09-28T21:47:26+02:00
  
      date:modify: 2015-09-28T21:47:26+02:00
  
      jpeg:colorspace: 2
  
      jpeg:quality: 92
  
      jpeg:sampling-factor: 2x2,1x1,1x1
  
      signature: ca599d8ad07c79b36837cb9f4811d83e236b8d4a4cdfada8d60c4aa330f28f38
  
    Artifacts:
  
      filename: f1.jpg
  
      verbose: true
  
    Tainted: False
  
    Filesize: 1.36KB
  
    Number pixels: 98K
  
    Pixels per second: 9.8MB
  
    User time: 0.000u
  
    Elapsed time: 0:01.009
  
    Version: ImageMagick 6.8.7-0 2013-10-28 Q16 http://www.imagemagick.org

  ---

  $ identify -verbose f2.jpg
  
  Image: f2.jpg
  
    Format: JPEG (Joint Photographic Experts Group JFIF format)
  
    Mime type: image/jpeg
  
    Class: DirectClass
  
    Geometry: 980x100+0+0
  
    Resolution: 72x72
  
    Print size: 13.6111x1.38889
  
    Units: PixelsPerInch
  
    Type: Palette
  
    Endianess: Undefined
  
    Colorspace: sRGB
  
    Depth: 8-bit
  
    Channel depth:
  
      red: 8-bit
  
      green: 8-bit
  
      blue: 8-bit
  
    Channel statistics:
  
      Red:
  
        min: 186 (0.729412)
  
        max: 254 (0.996078)
  
        mean: 242.844 (0.952329)
  
        standard deviation: 11.0845 (0.0434688)
  
        kurtosis: 4.24417
  
        skewness: -2.17102
  
      Green:
  
        min: 186 (0.729412)
  
        max: 254 (0.996078)
  
        mean: 242.844 (0.952329)
  
        standard deviation: 11.0845 (0.0434688)
  
        kurtosis: 4.24417
  
        skewness: -2.17102
  
      Blue:
  
        min: 186 (0.729412)
  
        max: 254 (0.996078)
  
        mean: 242.842 (0.952323)
  
        standard deviation: 11.0886 (0.0434848)
  
        kurtosis: 4.24235
  
        skewness: -2.17103
  
    Image statistics:
  
      Overall:
  
        min: 186 (0.729412)
  
        max: 254 (0.996078)
  
        mean: 242.843 (0.952327)
  
        standard deviation: 11.0859 (0.0434741)
  
        kurtosis: 4.24356
  
        skewness: -2.17102
  
    Colors: 91
  
    Histogram:
  
           2: (186,186,186) #BABABA grey73
  
           2: (187,187,187) #BBBBBB srgb(187,187,187)
  
           1: (189,189,189) #BDBDBD grey74
  
           5: (190,190,190) #BEBEBE grey
  
           2: (191,191,191) #BFBFBF grey75
  
          20: (192,192,192) #C0C0C0 silver
  
          28: (193,193,193) #C1C1C1 srgb(193,193,193)
  
          37: (194,194,194) #C2C2C2 grey76
  
          46: (195,195,195) #C3C3C3 srgb(195,195,195)
  
          59: (196,196,196) #C4C4C4 grey77
  
         108: (197,197,197) #C5C5C5 srgb(197,197,197)
  
         134: (198,198,198) #C6C6C6 srgb(198,198,198)
  
         145: (199,199,199) #C7C7C7 grey78
  
         188: (200,200,200) #C8C8C8 srgb(200,200,200)
  
         230: (201,201,201) #C9C9C9 grey79
  
         241: (202,202,202) #CACACA srgb(202,202,202)
  
         236: (203,203,203) #CBCBCB srgb(203,203,203)
  
         252: (204,204,204) #CCCCCC grey80
  
         240: (205,205,205) #CDCDCD srgb(205,205,205)
  
         243: (206,206,206) #CECECE srgb(206,206,206)
  
           1: (207,207,205) #CFCFCD srgb(207,207,205)
  
         250: (207,207,207) #CFCFCF grey81
  
           1: (208,208,206) #D0D0CE srgb(208,208,206)
  
         267: (208,208,208) #D0D0D0 srgb(208,208,208)
  
           2: (209,209,207) #D1D1CF srgb(209,209,207)
  
         226: (209,209,209) #D1D1D1 grey82
  
           3: (210,210,208) #D2D2D0 srgb(210,210,208)
  
         193: (210,210,210) #D2D2D2 srgb(210,210,210)
  
           5: (211,211,209) #D3D3D1 srgb(211,211,209)
  
         215: (211,211,211) #D3D3D3 LightGray
  
           7: (212,212,210) #D4D4D2 srgb(212,212,210)
  
         227: (212,212,212) #D4D4D4 grey83
  
          11: (213,213,211) #D5D5D3 srgb(213,213,211)
  
         250: (213,213,213) #D5D5D5 srgb(213,213,213)
  
           4: (214,214,212) #D6D6D4 srgb(214,214,212)
  
         291: (214,214,214) #D6D6D6 grey84
  
          16: (215,215,213) #D7D7D5 srgb(215,215,213)
  
         307: (215,215,215) #D7D7D7 srgb(215,215,215)
  
           1: (216,216,214) #D8D8D6 srgb(216,216,214)
  
         371: (216,216,216) #D8D8D8 srgb(216,216,216)
  
           4: (217,217,215) #D9D9D7 srgb(217,217,215)
  
         355: (217,217,217) #D9D9D9 grey85
  
           3: (218,218,216) #DADAD8 srgb(218,218,216)
  
         398: (218,218,218) #DADADA srgb(218,218,218)
  
           3: (219,219,217) #DBDBD9 srgb(219,219,217)
  
         404: (219,219,219) #DBDBDB grey86
  
           5: (220,220,218) #DCDCDA srgb(220,220,218)
  
         435: (220,220,220) #DCDCDC gainsboro
  
           1: (221,221,219) #DDDDDB srgb(221,221,219)
  
         489: (221,221,221) #DDDDDD srgb(221,221,221)
  
           2: (222,222,220) #DEDEDC srgb(222,222,220)
  
         569: (222,222,222) #DEDEDE grey87
  
           1: (223,223,221) #DFDFDD srgb(223,223,221)
  
         552: (223,223,223) #DFDFDF srgb(223,223,223)
  
           2: (224,224,222) #E0E0DE srgb(224,224,222)
  
         595: (224,224,224) #E0E0E0 grey88
  
           2: (225,225,223) #E1E1DF srgb(225,225,223)
  
         645: (225,225,225) #E1E1E1 srgb(225,225,225)
  
         736: (226,226,226) #E2E2E2 srgb(226,226,226)
  
           3: (227,227,225) #E3E3E1 srgb(227,227,225)
  
         646: (227,227,227) #E3E3E3 grey89
  
           1: (228,228,226) #E4E4E2 srgb(228,228,226)
  
         707: (228,228,228) #E4E4E4 srgb(228,228,228)
  
           1: (229,229,227) #E5E5E3 srgb(229,229,227)
  
         667: (229,229,229) #E5E5E5 grey90
  
           1: (230,230,228) #E6E6E4 srgb(230,230,228)
  
         759: (230,230,230) #E6E6E6 srgb(230,230,230)
  
         767: (231,231,231) #E7E7E7 srgb(231,231,231)
  
         788: (232,232,232) #E8E8E8 grey91
  
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• Smooth scale animation in ffmpeg

  3 août 2018, par Makes

  How can I smooth the animations of scale ?

  ffmpeg -i bg.mp4 -loop 1 -i blank.png -filter_complex "[1:v]drawtext=fontfile=ariblk.ttf:text=EXAMPLE:x=(main_w-text_w)/2:y=(main_h-max_glyph_a)/2:fontcolor=0xFFFFFF:fontsize=200:alpha=1[txt];[txt]sendcmd=f=data.cmd,scale=w=0:h=-1[txtscale];[0:v][txtscale]overlay=(main_w-overlay_w)/2:(main_h-overlay_h)/2:shortest=1" preview.mp4

  Video resolution 1280 * 720 -> scale w = ’12.8 * X’, X - scale value (data from the After Effects)

  data.cmd

  0   scale w '   12.8*   72  '; 0.040    scale w '   12.8*   72.3944 '; 0.080    scale w '   12.8*   72.7887 '; 0.120    scale w '   12.8*   73.1831 '; 0.160    scale w '   12.8*   73.5775 '; 0.200    scale w '   12.8*   73.9718 '; 0.240    scale w '   12.8*   74.3662 '; 0.280    scale w '   12.8*   74.7606 '; 0.320    scale w '   12.8*   75.1549 '; 0.360    scale w '   12.8*   75.5493 '; 0.400    scale w '   12.8*   75.9437 '; 0.440    scale w '   12.8*   76.338  '; 0.480    scale w '   12.8*   76.7324 '; 0.520    scale w '   12.8*   77.1268 '; 0.560    scale w '   12.8*   77.5211 '; 0.600    scale w '   12.8*   77.9155 '; 0.640    scale w '   12.8*   78.3099 '; 0.680    scale w '   12.8*   78.7042 '; 0.720    scale w '   12.8*   79.0986 '; 0.760    scale w '   12.8*   79.493  '; 0.800    scale w '   12.8*   79.8873 '; 0.840    scale w '   12.8*   80.2817 '; 0.880    scale w '   12.8*   80.6761 '; 0.920    scale w '   12.8*   81.0704 '; 0.960    scale w '   12.8*   81.4648 '; 1.000    scale w '   12.8*   81.8592 '; 1.040    scale w '   12.8*   82.2535 '; 1.080    scale w '   12.8*   82.6479 '; 1.120    scale w '   12.8*   83.0423 '; 1.160    scale w '   12.8*   83.4366 '; 1.200    scale w '   12.8*   83.831  '; 1.240    scale w '   12.8*   84.2254 '; 1.280    scale w '   12.8*   84.6197 '; 1.320    scale w '   12.8*   85.0141 '; 1.360    scale w '   12.8*   85.4085 '; 1.400    scale w '   12.8*   85.8028 '; 1.440    scale w '   12.8*   86.1972 '; 1.480    scale w '   12.8*   86.5915 '; 1.520    scale w '   12.8*   86.9859 '; 1.560    scale w '   12.8*   87.3803 '; 1.600    scale w '   12.8*   87.7746 '; 1.640    scale w '   12.8*   88.169  '; 1.680    scale w '   12.8*   88.5634 '; 1.720    scale w '   12.8*   88.9577 '; 1.760    scale w '   12.8*   89.3521 '; 1.800    scale w '   12.8*   89.7465 '; 1.840    scale w '   12.8*   90.1408 '; 1.880    scale w '   12.8*   90.5352 '; 1.920    scale w '   12.8*   90.9296 '; 1.960    scale w '   12.8*   91.3239 '; 2.000    scale w '   12.8*   91.7183 '; 2.040    scale w '   12.8*   92.1127 '; 2.080    scale w '   12.8*   92.507  '; 2.120    scale w '   12.8*   92.9014 '; 2.160    scale w '   12.8*   93.2958 '; 2.200    scale w '   12.8*   93.6901 '; 2.240    scale w '   12.8*   94.0845 '; 2.280    scale w '   12.8*   94.4789 '; 2.320    scale w '   12.8*   94.8732 '; 2.360    scale w '   12.8*   95.2676 '; 2.400    scale w '   12.8*   95.662  '; 2.440    scale w '   12.8*   96.0563 '; 2.480    scale w '   12.8*   96.4507 '; 2.520    scale w '   12.8*   96.8451 '; 2.560    scale w '   12.8*   97.2394 '; 2.600    scale w '   12.8*   97.6338 '; 2.640    scale w '   12.8*   98.0282 '; 2.680    scale w '   12.8*   98.4225 '; 2.720    scale w '   12.8*   98.8169 '; 2.760    scale w '   12.8*   99.2113 '; 2.800    scale w '   12.8*   99.6056 '; 2.840    scale w '   12.8*   100 '; 

  

• What Is Data Misuse & How to Prevent It ? (With Examples)

  13 mai 2024, par Erin

  Your data is everywhere. Every time you sign up for an email list, log in to Facebook or download a free app onto your smartphone, your data is being taken.

  This can scare customers and users who fear their data will be misused.

  While data can be a powerful asset for your business, it’s important you manage it well, or you could be in over your head.

  In this guide, we break down what data misuse is, what the different types are, some examples of major data misuse and how you can prevent it so you can grow your brand sustainably.

  What is data misuse ?

  Data is a good thing.

  It helps analysts and marketers understand their customers better so they can serve them relevant information, products and services to improve their lives.

  But it can quickly become a bad thing for both the customers and business owners when it’s mishandled and misused.

  

  Data misuse is when a business uses data outside of the agreed-upon terms. When companies collect data, they need to legally communicate how that data is being used. 

  Who or what determines when data is being misused ?

  Several bodies :

  • User agreements
  • Data privacy laws
  • Corporate policies
  • Industry regulations

  There are certain laws and regulations around how you can collect and use data. Failure to comply with these guidelines and rules can result in several consequences, including legal action.

  Keep reading to discover the different types of data misuse and how to prevent it.

  3 types of data misuse

  There are a few different types of data misuse.

  If you fail to understand them, you could face penalties, legal trouble and a poor brand reputation.

  

  1. Commingling

  When you collect data, you need to ensure you’re using it for the right purpose. Commingling is when an organisation collects data from a specific audience for a specific reason but then uses the data for another purpose.

  One example of commingling is if a company shares sensitive customer data with another company. In many cases, sister companies will share data even if the terms of the data collection didn’t include that clause.

  Another example is if someone collects data for academic purposes like research but then uses the data later on for marketing purposes to drive business growth in a for-profit company.

  In either case, the company went wrong by not being clear on what the data would be used for. You must communicate with your audience exactly how the data will be used.

  2. Personal benefit

  The second common way data is misused in the workplace is through “personal benefit.” This is when someone with access to data abuses it for their own gain.

  The most common example of personal benefit data muse is when an employee misuses internal data.

  While this may sound like each instance of data misuse is caused by malicious intent, that’s not always the case. Data misuse can still exist even if an employee didn’t have any harmful intent behind their actions. 

  One of the most common examples is when an employee mistakenly moves data from a company device to personal devices for easier access.

  3. Ambiguity

  As mentioned above, when discussing commingling, a company must only use data how they say they will use it when they collect it.

  A company can misuse data when they’re unclear on how the data is used. Ambiguity is when a company fails to disclose how user data is being collected and used.

  This means communicating poorly on how the data will be used can be wrong and lead to misuse.

  One of the most common ways this happens is when a company doesn’t know how to use the data, so they can’t give a specific reason. However, this is still considered misuse, as companies need to disclose exactly how they will use the data they collect from their customers.

  Laws on data misuse you need to follow

  Data misuse can lead to poor reputations and penalties from big tech companies. For example, if you step outside social media platforms’ guidelines, you could be suspended, banned or shadowbanned.

  But what’s even more important is certain types of data misuse could mean you’re breaking laws worldwide. Here are some laws on data misuse you need to follow to avoid legal trouble :

  General Data Protection Regulation (GDPR)

  The GDPR, or General Data Protection Regulation, is a law within the European Union (EU) that went into effect in 2018.

  The GDPR was implemented to set a standard and improve data protection in Europe. It was also established to increase accountability and transparency for data breaches within businesses and organisations.

  The purpose of the GDPR is to protect residents within the European Union.

  The penalties for breaking GDPR laws are fines up to 20 million Euros or 4% of global revenues (whatever the higher amount is).

  The GDPR doesn’t just affect companies in Europe. You can break the GDPR’s laws regardless of where your organisation is located worldwide. As long as your company collects, processes or uses the personal data of any EU resident, you’re subject to the GDPR’s rules.

  If you want to track user data to grow your business, you need to ensure you’re following international data laws. Tools like Matomo—the world’s leading privacy-friendly web analytics solution—can help you achieve GDPR compliance and maintain it.

  With Matomo, you can confidently enhance your website’s performance, knowing that you’re adhering to data protection laws. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  California Consumer Privacy Act (CCPA)

  The California Consumer Privacy Act (CCPA) is another important data law companies worldwide must follow.

  Like GDPR, the CCPA is a data privacy law established to protect residents of a certain region — in this case, residents of California in the United States.

  The CCPA was implemented in 2020, and businesses worldwide can be penalised for breaking the regulations. For example, if you’re found violating the CCPA, you could be fined $7,500 for each intentional violation.

  If you have unintentional violations, you could still be fined, but at a lesser fee of $2,500.

  The Gramm-Leach-Bliley Act (GLBA)

  If your business is located within the United States, then you’re subject to a federal law implemented in 1999 called The Gramm-Leach-Bliley Act (GLB Act or GLBA).

  The GLBA is also known as the Financial Modernization Act of 1999. Its purpose is to control the way American financial institutions handle consumer data. 

  In the GLBA, there are three sections :

  1. The Financial Privacy Rule : regulates the collection and disclosure of private financial data.
  2. Safeguards Rule : Financial institutions must establish security programs to protect financial data.
  3. Pretexting Provisions : Prohibits accessing private data using false pretences.

  The GLBA also requires financial institutions in the U.S. to give their customers written privacy policy communications that explain their data-sharing practices.

  4 examples of data misuse in real life

  If you want to see what data misuse looks like in real life, look no further.

  Big tech is central to some of the biggest data misuses and scandals.

  

  Here are a few examples of data misuse in real life you should take note of to avoid a similar scenario :

  1. Facebook election interference

  One of history’s most famous examples of data misuse is the Facebook and Cambridge Analytica scandal in 2018.

  During the 2018 U.S. midterm elections, Cambridge Analytica, a political consulting firm, acquired personal data from Facebook users that was said to have been collected for academic research.

  Instead, Cambridge Analytica used data from roughly 87 million Facebook users. 

  This is a prime example of commingling.

  The result ? Cambridge Analytica was left bankrupt and dissolved, and Facebook was fined $5 billion by the Federal Trade Commission (FTC).

  2. Uber “God View” tracking

  Another big tech company, Uber, was caught misusing data a decade ago. 

  Why ?

  Uber implemented a new feature for its employees in 2014 called “God View.”

  The tool enabled Uber employees to track riders using their app. The problem was that they were watching them without the users’ permission. “God View” lets Uber spy on their riders to see their movements and locations.

  The FTC ended up slapping them with a major lawsuit, and as part of their settlement agreement, Uber agreed to have an outside firm audit their privacy practices between 2014 and 2034.

  

  3. Twitter targeted ads overstep

  In 2019, Twitter was found guilty of allowing advertisers to access its users’ personal data to improve advertisement targeting.

  Advertisers were given access to user email addresses and phone numbers without explicit permission from the users. The result was that Twitter ad buyers could use this contact information to cross-reference with Twitter’s data to serve ads to them.

  Twitter stated that the data leak was an internal error. 

  4. Google location tracking

  In 2020, Google was found guilty of not explicitly disclosing how it’s using its users’ personal data, which is an example of ambiguity.

  The result ?

  The French data protection authority fined Google $57 million.

  8 ways to prevent data misuse in your company

  Now that you know the dangers of data misuse and its associated penalties, it’s time to understand how you can prevent it in your company.

  

  Here are eight ways you can prevent data misuse :

  1. Track data with an ethical web analytics solution

  You can’t get by in today’s business world without tracking data. The question is whether you’re tracking it safely or not.

  If you want to ensure you aren’t getting into legal trouble with data misuse, then you need to use an ethical web analytics solution like Matomo.

  With it, you can track and improve your website performance while remaining GDPR-compliant and respecting user privacy. Unlike other web analytics solutions that monetise your data and auction it off to advertisers, with Matomo, you own your data.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  2. Don’t share data with big tech

  As the data misuse examples above show, big tech companies often violate data privacy laws.

  And while most of these companies, like Google, appear to be convenient, they’re often inconvenient (and much worse), especially regarding data leaks, privacy breaches and the sale of your data to advertisers.

  Have you ever heard the phrase : “You are the product ?” When it comes to big tech, chances are if you’re getting it for free, you (and your data) are the products they’re selling.

  The best way to stop sharing data with big tech is to stop using platforms like Google. For more ideas on different Google product alternatives, check out this list of Google alternatives.

  3. Identity verification 

  Data misuse typically isn’t a company-wide ploy. Often, it’s the lack of security structure and systems within your company. 

  An important place to start is to ensure proper identity verification for anyone with access to your data.

  4. Access management

  After establishing identity verification, you should ensure you have proper access management set up. For example, you should only give specific access to specific roles in your company to prevent data misuse.

  5. Activity logs and monitoring

  One way to track data misuse or breaches is by setting up activity logs to ensure you can see who is accessing certain types of data and when they’re accessing it.

  You should ensure you have a team dedicated to continuously monitoring these logs to catch anything quickly.

  6. Behaviour alerts 

  While manually monitoring data is important, it’s also good to set up automatic alerts if there is unusual activity around your data centres. You should set up behaviour alerts and notifications in case threats or compromising events occur.

  7. Onboarding, training, education

  One way to ensure quality data management is to keep your employees up to speed on data security. You should ensure data security is a part of your employee onboarding. Also, you should have regular training and education to keep people informed on protecting company and customer data.

  8. Create data protocols and processes 

  To ensure long-term data security, you should establish data protocols and processes. 

  To protect your user data, set up rules and systems within your organisation that people can reference and follow continuously to prevent data misuse.

  Leverage data ethically with Matomo

  Data is everything in business.

  But it’s not something to be taken lightly. Mishandling user data can break customer trust, lead to penalties from organisations and even create legal trouble and massive fines.

  You should only use privacy-first tools to ensure you’re handling data responsibly.

  Matomo is a privacy-friendly web analytics tool that collects, stores and tracks data across your website without breaking privacy laws.

  With over 1 million websites using Matomo, you can track and improve website performance with :

  • Accurate data (no data sampling)
  • Privacy-friendly and compliant with privacy regulations like GDPR, CCPA and more
  • Advanced features like heatmaps, session recordings, A/B testing and more

  Try Matomo free for 21-days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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