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function replace(ref) {
    ref = {};           // this code does _not_ affect the object passed
}

function update(ref) {
    ref.key = 'newvalue';  // this code _does_ affect the _contents_ of the object
}

var a = { key: 'value' };
replace(a);  // a still has its original value - it's unmodfied

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function isObjNotArray(obj) {
  return obj && typeof obj == 'object' && !Array.isArray(obj)
}

function isIterable(obj) {
  let type = false
  if (isObjNotArray(obj)) type = 'obj'
  else if (Array.isArray(obj)) type = 'arr'
  return type
}

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Binary obfuscation employed thoroughly. It is prevalent to note the distinction between preventing reverse engineering and preventing fingerprinting. A tool that makes software incredibly difficult to reverse-engineer often involves binary obfuscation structures that would be trivial to fingerprint. 

 

Employ memory and data scraping prevention. If a program saves a string in memory, ensure it is represented in a randomized, encoded manner to avoid behaving as a signature via memory scraper. If the original executable contains any image or alternate data, ensure this is obfuscated randomly and effectively before delivery to the target. 

 

Evade AV emulation (pre-execution) detection products via black box attacks. See AVLeak, Blackthorne et al (video presentation if preferred, Blackhat 2016). 

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Set-mppreference –DisableRealtimeMonitoring $TRUE 
new-item "HKLM:\Software\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\sethc.exe" 
Get-psdrive 
Start-transcript | out-null 
Set-ExecutionPolicy Unrestricted 
Update-Help -Force 
-ErrorAction SilentlyContinue 
2>$null 2>\dev\null 
set-alias edit notepad.exe 
Get-process | get-member 

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py Tensorflow\models\research\object_detection\export_inference_graph.py     --input_type image_tensor     --pipeline_config_path Tensorflow\workspace\models\my_ssd_mobnet\pipeline.config     --trained_checkpoint_prefix Tensorflow\workspace\pre-trained-models\ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8\checkpoint\ckt-5.data-00000-of-00001     --output_directory Tensorflow\workspace\models\my_ssd_mobnet\export
2021-07-16 12:06:25.701510: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
2021-07-16 12:06:28.075448: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll
2021-07-16 12:06:28.093311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2080 SUPER computeCapability: 7.5
coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 462.00GiB/s
2021-07-16 12:06:28.093541: I t

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py Tensorflow\models\research\object_detection\export_inference_graph.py
--input_type image_tensor
--pipeline_config_path Tensorflow\workspace\models\my_ssd_mobnet\pipeline.config
--trained_checkpoint_prefix Tensorflow\workspace\pre-trained-models\ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8\checkpoint\ckpt-0.data-00000-of-00001
--output_directory Tensorflow\workspace\models\my_ssd_mobnet\export

full logs:

py Tensorflow\models\research\object_detection\export_inference_graph.py     --input_type image_tensor     --pipeline_config_path Tensorflow\workspace\models\my_ssd_mobnet\pipeline.config     --trained_checkpoint_prefix Tensorflow\workspace\pre-trained-models\ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8\checkpoint\ckpt-0.data-00000-of-00001     --output_directory Tensorflow\workspace\models\my_ssd_mobnet\export
2021-07-15 09:40:24.482953: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll

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 2021-07-11 02:25:42.869766: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
    py Tensorflow\models\research\object_detection\model_main_tf2.py --model_dir=Tensorflow\workspace\models\my_ssd_mobnet --pipeline_config_path=Tensorflow\workspace\models\my_ssd_mobnet\pipeline.config --num_train_steps=100
    2021-07-11 02:25:44.989884: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
    2021-07-11 02:25:47.588384: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll
    2021-07-11 02:25:47.605286: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
    pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2080 SUPER computeCapability: 7.5
    coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 462.00GiB/s
    2021-07-11 02:25:47.605366: I tensorflow/stream_ex

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# Lint as: python2, python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

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const readBit = (buffer, i, bit) => {
  return (buffer[i] >> bit) % 2
}
const setBit = (i, bit, value) => {
  if (value == 0) {
    b8[i] &= ~(1 << bit)
  } else {
    b8[i] |= (1 << bit)
  }
  return b8

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export default class extends Element {
  constructor(index, source, focused) {
    super();
    this.index = index;
    this.source = source;
    this.self = {};
    this.source.cards.push(this.self);
    this.source.cardRefs.push(this);
    this.flipped = false;
    this.nextCard = false;