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Hello friends! I have been working on an encryption algorithm in autoit as a proof of concept for some time now. Basically the algorithm uses a progressive recursion to encode data inside a matrix using a key that changes according to the date-time of the system, which is extracted from a larger key array. Recently after a drive failure, I lost the source and had to start from scratch, now I can't quite get it working the way it was before, and I can't see what I'm doing wrong, if anyone who understands matrix math or encryption could help I would much appreciate it. The problem is that the values returned by the decryption (extraction) process are way too big.

 

I have figured out the solution to my problem, it was a typo, please disregard this thread.

I will post my project into example scripts when it's ready.

 

 

 

Edited by AlecSadler
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'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' $BG_Image &= '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' $BG_Image &= 'QqtdU5DJFzzE0h0VgCDmCcdjrHBkuexM9zYIAy6GXOWICPE+BFnXWPJOqkCB7rNKn9xeEDRKrUVHDTISxFkadrsqsoQg9jIKjj67PqqQ+DS+HGMsyhxFugpUn1OQybcIUgESohx6HGsUIUsaVfqw41cVsvTyY6gy9LjWINY8QVZhwAUpovMx1uT2JDQE1W56TZDgM6p0J8qPK12DhzlxUWQIMgLEVVtzhgAQVX+lP5PJrVlrJAigQuIBEhHicnQ3hiJn6yHJZaAVdvx9eA9ObyM+rPcEIXCBCAydz3knd2eFUytQvQY5cjfk0wMkeiekiHvgckT9EdMhQw+7jS+GII3uhmqBQqYgky/YLoWqgEJ6CZItQJzQY+THhXFIb+jhZgxCl/41b0MQIlGIQAGozASZ3JuHiKD1WgsEDLYS5McM8Qrd+J0fpsXjlyOP8OS6Zeq8bLx1oggxQ0hEBQ01I9cpyOT2gihQWi8goVGEuCGfbdQg2wiQ9/Uv/xFePNyJwb9J02uQdc2ZWKSvdleVNAWZ3H+IZQlSLEJEYoiVTZCoP0yRrTXjeiKWdftokfE2SwZjkeJg1CDHaoLAqQDKFGRyd0EsNiAo5gd+JYiHR/R/dtw++qTgFltuvV24FCD2frFmfiTzgwDTUQHoFGRydxKJ1QUCmB7c/RizhO/f0+jbeb4ffQXJw8NkYG9dl3MYcmFpWIBkIkYBKmqFzg0hk7uzCmgVkSosTNkYE4WuxsDrj5Ye7789Qewl0iMSJK6oR65u/B3TIDlThrgeWtGYgkxuDqEKiRSBUH/K24+vNjviuibIe+y6be3d+vVELOe8nh63XJ9hJQsQgguCUoAyBZncHDIzhAplg9ZsHGMO/cJ7Od+mx9sy5Pwbogw34vX3sQ3BYq3RS5CeIDBDpiCTu7OiEhXpR/50VhckxbjK1BgZ8j7DkbOxvU/jfZUkHDHOgcWIC3JYgKiguCCi0CnI5Oas4pTStKCVKGfym72s' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '4cYLwtfWplfhNwhGVK+grB6Q5L6bUdEovmKD81b3KSuBP3d6j75xdWV+aU5XVSgAIZ6VdwG5o5uNzCwiSS1IBUWEkuFizOFCB6Z88RDY2vTsRjC4WPiMAg2FI5TqLkRJZH0hjXU3y2wRQucODgRQQsbOnd6jb9qXaG5+jZe5CGFHlbuV033MBIcqdiJOGnaadLDIigCC3Ft1Qi9zf3QeiUFR2NC0nOw9UL+ubrdjZtRjPga+SuFKsKXXwizuAAiAg1JCm3In8R9/Ozp66J+j1HromE55SJfUipQLD0UPVYmNSt4UcXEhUzKEqRNESk5D10g+ZAMn9RORjhVuq6wRLbKktyszJtWOYyQyCyLwGN9wRwudNdbR88l5mdkUGiof0qxyCTfdhQkqJ0SZSQV578Py22QuOlxqKPCryAQmoqcJK4ji8bxwlMk3GAOE3l+x8Bp2KYbx2EMWxiTSW/ch95SNUWeNdfToPvidXcFRVI+kcKRjaTjABVYWNvqgHpzwCQkkqBmuqyR6dulRrcjtOlcYELI16CUgjEv4URj9wVLF4KZU2WUl4yMyiPl1lDUUmx57eo/OzSu75FMVJH2yOup7s92ru3ziZC8/p2Lx8cpGI4sPlm+VQnYjqFEJLFdbZ57EYbeNAS4yoxYtkNFF5M1QOWwMXhphqt57FYNw1Ddv7rV9jXV0VlfxsnkNgwr3ykmpyKoCDtZVmXWFDDQ1SQGpjACIot2QYANbnO2ik6mYShOEsdNCGMVN3aGIqUs16tGGiB/bfsShmDY85cOiwyH4njuxUJ3tJkesrj6mTb/GnJPeOn1tzq1c7QOQVCmQkWayxuoFVmOwseD4TCyuloIYPaYiaEwMGvjrX06mGcGAUS+s+jMxOL1MxGiW1l1B8YDQHmx82FCROWuso3t1FV4mPOw66DRYFDmrqVqLq0nh0AkYiI6eFRbFY2/Gk1R2JBUSAKiVmIeaBQUV1qluTN6ojNWVG6TUEBRUGitd' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= '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' $BG_Image &= 'ikL3TwxMykzSXS5gusptiTOZtDnTCdXp9SbfNTm835JfQkuXqVgxk1he3tev/ErK/PtIz+N4WoOc+PEx3d3vtqOAEmwmSUjeXSVt5HcbklEg2UnFv5s5tBWNvicr7vLcd/INj3wXCTvAxMxmsOUo2zlTJ3IleSqNbk40EEtv+Hp2XBiz0sk74forGyhdy64yPyLS2UAEvm3UKJHn4RCh54X0Fib8EAtyD5c66K0zCRQ46R3QVuFRvyDiPK1MVIkRIj3d5cjanq4Y5THYcxMpMYTxFk0llZWNqSaDOUg1ViT3WkEuV+Rwgu29Lld0fUXVaZF+BZX3jMUYOTSghF40cZVkLL5keqjx/CgNcnt5D6IlDERNbqFU/vkVe8WtTUjJIp0LykI0mQwFe/+SBP4Sd4dKog95OE/ThBKvO2V1jt0+ucl7ooMuuhXgg6Ru0BfXDEk+5m6y0g5mEiS1NbF295+0WPw4DXI7sWg/b+ZpiBnkn0zd35DQoJ/NMJ2dHpb7KaIypDNDsOT10Mnhhmh0FaZGXms1lgeSiOv8LMsM241ZqregSGccW/vevKT6BSwsgpCrfaktL6aOFxk69aLH30ihBYjnsWWT9fFz0r8f/xyW6B16SAQnZlcZGHWF8IclDrwq7XeUDN8QJDgFQNJqlBddrBP7QOUG68oOqXVeeNlA+wAL91SrOx+lHCm8FOl7x8hSDMjU8w///NlOcDlcFPI0Tk7enw+Ycnt3bDiAhqLSlTicRP4yQSdIP9G0aIg6F3cUlfr2ECSdGkzIOKZ8WKRALIPfwXob3srtbd5/R4yJkJJcYNroCoZaMzZ60fxagqQY+W0c9GR3yb/bSM8XQ3pxZMcHEOTuqmgLgsH6KmLLWv+dH2UGlbXKOf6xQ36dvViR5bk1titMLZVUqBS1nwavS6cYOjdGBAG0vFqSQ3nx4M1oXNaI7qvWimm0Gr9bK/EjiNNGroWMm3IkxMgu69sI8j/twS5+biz0fwAA' $BG_Image &= 'AABJRU5ErkJggg==' Local $bString = _WinAPI_Base64Decode($BG_Image) If @error Then Return SetError(1, 0, 0) $bString = Binary($bString) If $bSaveBinary Then Local Const $hFile = FileOpen($sSavePath & "\Neo_Matrix_800x500_cut_32c.png", 18) If @error Then Return SetError(2, 0, $bString) FileWrite($hFile, $bString) FileClose($hFile) EndIf Return $bString EndFunc ;==>_BG_Image You need The Matrix Sound.au3 which is in the Zip archive (see below) to run the code properly!
       
      Download: The Matrix v1.31 (incl. sound), without sound The Matrix v1.30
       
      Can you decipher the code?
       
    • By RTFC
      CodeCrypter enables you to encrypt scripts without placing the key inside the script.
      This is because this key is extracted from the user environment at runtime by, for example:
      password user query any macro (e.g., @username) any AutoIt function call any UDF call some permanent environment variable on a specific machine (and not created by your script) a server response a device response anything else you can think of, as long as it's not stored in the script any combination of the above You need several scripts to get this to work, and they are scattered over several threads, so here's a single bundle that contains them all (including a patched version of Ward's AES.au3; with many thanks to Ward for allowing me to include this script here):
      Latest version: 3.4 (3 Dec 2021): please follow this link.
      Note: if you experience issues under Win8/8.1 (as some users have reported), please upgrade to Win10 (or use Win7) if you can; as far as I can tell, the scripts in the bundle all work under Win7 & Win10 (and XP). Moreover, I have no access to a Win8 box, so these issues will not be fixed, at least not by yours truly.
       
      How the bits and pieces fit together:
      CodeCrypter is a front-end for the MCF UDF library (you need version 1.3 or later). Its thread is here:
      '?do=embed' frameborder='0' data-embedContent>>
      The MCF package (also contained in the CodeScannerCrypter bundle) contains MCF.au3 (the library itself) plus a little include file called MCFinclude.au3. The latter you have to include in any script you wish to encrypt. Any code preceding it will not be encrypted, any code following it will be encrypted. You define the dynamic key inside MCFinclude.au3, in the UDF: _MCFCC_Init().
      From the same post you can download an MCF Tutorial which I heartily recommend, because encrypting a script requires a number of steps in the right order, namely:
      In MCFinclude.au3, define and/or choose your dynamic key(s) (skip this step = use default setting) include MCFinclude.au3 in your target script Run CodeScanner (version 2.3+) on your target script, with setting WriteMetaCode=True (see '?do=embed' frameborder='0' data-embedContent>>), then close CodeScanner. Start CodeCrypter press the Source button to load your target file enable Write MCF0 (tick the first option in Main Settings) Enable "Encrypt" (last option in the Main Settings) Go to the Tab Encrypt and set up the encryption the way you want (skip this = use default settings) Return to Main Tab and press "Run" if all goes well, a new script called MCF0test.au3 is created in the same directory as your target. It has no includes and no redundant parts. Please check that it works as normal. (see Remarks if not) It all sounds far more complicated than it is, really.
      Not convinced? Check out:
      a simple HowTo Guide: HowToCodeCrypt.pdf an updated and extended Q & A pdf (FAQ, also included in the bundle) to help you get started:CodeCrypterFAQ.pdf For additional explanations/examples in response to specific questions by forum members (how it works, what it can/cannot do), see elsewhere in this thread, notably:
      Simple analogy of how it works: post #53, second part General Explanation and HowTo: post #9, 51, 75, 185/187, 196, 207, 270, 280 (this gets a bit repetitive) BackTranslation: post #179 Obfuscation: post #36 (general), 49 (selective obfuscation) Specific features and fixes: post #3 (security), 84 (redefining the expected runtime response), 169 (Curl Enum fix), 185/187 (using license keys), 194 (replacing Ward's AES UDF with different encryption/decryption calls), 251 (AV detection issue), 262 (extract key contents to USB on different target machine prior to encryption) Limitations: post #26 (@error/@extended), 149 (FileInstall), 191 (AES.au3 on x64) Not recommended: post #46/249 (static encryption), 102 (programme logic error), 237 (parsing password via cmdline)  
      Technical notes:
      BackTranslation is a test to check that the MetaCode translation worked. Skip it at your peril. It also turns your multi-include composite script into a single portable file without redundant parts (you can opt to leave the redundant parts in, if you want).
      CodeCrypter can also obfuscate (vars and UDF names) and replace strings, variable names and UDF names with anything else you provide, for  example, for language translation). After CodeScanner separates your target's structure from its contents, CodeCrypter (actually MCF, under the hood) can change any part, and then generate a new script from whichever pieces you define. See the MCF Tutorial for more explanation and examples.
      Encryption currently relies on Ward's excellent AES UDF and TheXman's sophisticated CryptoNG bundle. You can replace these with any other algorithm you like (but this is not trivial to do: edit MCFinclude.au3 UDF _MCFCC(), and MCF.au3 UDF _EncryptEntry(), see post #194 in this thread). AES by Ward, and CryptoNG by TheXman are also included in the bundle (with many thanks to Ward and TheXman for graciously allowing me to republish their outstanding work).
      Going to lie down now...
      RT
       
    • By RTFC
      The CodeScannerCrypterBundle (ca. 2.9 MB unzipped) contains the following UDFs and utilities:
      CodeScanner: analyse AutoIt script structure and content, identify potential issues, generate MCF data files CodeCrypter: front-end GUI for the MCF library, for script encryption (without storing the decryption key(s) in the script!) MetaCodeFile UDF (MCF library): for analysis and user-defined alterations of AutoIt script structure and content MCFinclude.au3: #include this UDF in any AutoIt script that you wish CodeCrypter to process CryptoNG, by TheXman; encryption UDF using Bcrypt dll calls (32/64-bit; various algorithms) StoreCCprofile.au3/readCSdatadump.au3/helloworld.au3: auxiliary utilities and example script HowToCodeCrypt.pdf: a simple guide in five steps CodeCrypterFAQ.pdf: questions and answers, partly based upon exchanges in the CodeCrypter thread. MetaCodeTutorial.pdf: the MCF engine explained; useful for encryption, GUI translation, code translation, and much more... Please follow the links for additional information.
    • By RTFC
      The Eigen C++ template library is a great environment for matrix computing; it is fast, reliable, extensive, and well-documented. It is also completely free, and does not rely on any external dependencies. Unfortunately for AutoIt users, the term “template library” implies that any functions you call are only instantiated upon compilation (in C++). That means there's nothing to hook into.
      To make Eigen ’s most important functionality directly accessible from within AutoIt scripts (version 3.3.12+, download it here), I developed the Eigen4AutoIt environment. It runs on Windows and under Wine also on Linux and Mac (Ubuntu, Debian, Fedora, and MacOS supported by WineHQ), and SUSE, Slackware, and FreeBSD supported by the distros).
      >Download the latest version

      It consists of:
      1) Eigen4AutoIt.au3
      an AutoIt library of wrapper functions that contain extensive bounds checks, matrix management, file I/O, type conversion, and precision control, and two-way data exchange with files and native AutoIt arrays;
      2) Eigen-wrapper dlls (EigenDense.dll, EigenDense_x64.dll)
      re-maps matrices created in AutoIt as Eigen Matrix objects, then calls Eigen’s powerful core functions; in the spirit of open-source, the full C++ source code I wrote is included in the bundle (see subdirectory "source"). The basic functions consist of a single Eigen call; decompositions and statistics are more involved. 3) Additional materials:
      the user-interactive, animated Function Selector and MatrixViewer tools the MatrixFileConverter to read/write E4A matrices from/to .csv ASCII, Excel, and Xbase files. three libraries of scientific and mathematical constants online Help, with example code for every function Large (.chm) Help file (for offline access) Quickstart Manual (11-page pdf, updated) Test suite Tutorials from Basics to Advanced Please note:
      none of this is part of Eigen's own distribution you only need this bundle; you do not need to install Eigen. How it works:
      No matrix content is ever transferred, only memory pointers, meaning computations in AutoIt are virtually as fast as in Eigen’s native environment, with the added advantage of not having to be compiled first. The drawback is that Eigen's native ad-hoc expression templates (and their internal optimisations) cannot be exploited here; you have to construct your operations with the basic building blocks. These provide matrix creation, I/O, cellwise operations, reduction, multiplication, transformation, decomposition (LU, Householder, Choleski, and Jacobi SVD; these include general linear solvers) and a small statistics module by yours truly.

      IMPORTANT: Posting Rules for this thread:
       
      1) Do not post or PM me any matrix-, maths-, or Eigen-related questions. Eigen has its own User Forum for that (or try math.stackExchange.com). I am not your maths guru! If you post such questions, I will either ignore your post or remind you of this rule.

      2) Do not post or PM me your data sets and/or non-working Eigen4AutoIt scripts; I will not analyse your data or fix your scripts for you! There are many reasons why a linear algebra procedure might fail to produce the answer you expect. You are wielding great mathematical power here, so exploit the fantastic internet resources at your fingertips and learn how to use it. To get you started, I've listed a few video tutorials and other helpful materials in the header remarks of Eigen4AutoIt.au3. Also check out the test scripts, the Tutorials, and the Help file.

      3) I do warmly welcome all of the following:
      remarks, advice, suggestions for improvements, encouragement, cash; bug reports re. the Eigen4AutoIt interface (individual functions that don't work the way they should) and/or the associated dll code (ditto); your own working Eigen4AutoIt templates of general use that you'd like to see implemented in a future release. Regarding that last item, have a look at my PCA tutorial. After the step-by-step stage, I summarise the entire procedure in a "mini script" of Eigen4AutoIt calls. Then I introduce the two internal PCA functions I developed, which replace that script with two simple calls. You can do the same thing, and submit your own functional Eigen4AutoIt script in this thread. If I consider it of general use and can implement it, it may become a new Eigen4AutoIt function in the next release (with source acknowledgement, of course). This means that you'd get a precompiled dll version of your script that will likely run faster and be simpler to call. Thereby this thread would become an Eigen4AutoIt Example Scripts mini forum. It's just a thought.
       
      >Download the latest version (uncompressed size: 38.4 MB)
       
      How to Install
      You need AutoIt version 3.3.12 or later. Simply run the installer, navigate to the tutorial subdirectory, open the first tutorial ("Basics") in Scite, and start it. This shows basic matrix I/O and mode switching (single versus double precision). If that runs, you're in business.
      NB to leverage the full power of x64 features, you'll also need to have the full Scite4AutoIt3 package installed. For more info, see sections "Bitness" and "Shared Memory" in the Help, main topic: "Work Environment" page.
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