My personal code and solution to the Synacor Challenge from 2012 OSCON.

Overview

Synacor OSCON Challenge Solution (2012)

This repository contains my code and solution to solve the Synacor OSCON 2012 Challenge.

If you are interested in checking out or trying the challenge for yourself, it can be found online still here:

https://challenge.synacor.com/

Notes

Firstly, please understand this is an old challenge. I am not the first to solve it, not even close, and this was solely done because a friend suggested it to me on Discord this past week. I never saw the challenge before and since it involved implementation of a VM, it was something I was interested in checking out since it has been a topic I've been involved in recently.

Next, the challenge is still online and fully functional. Because of that, it is important to note that if you do sign up and decide to try the challenge, the information in this solution will work but the flags (codes) you need will be different. The challenge generates unique flags for each player. (The challenge.bin data file is unique to each player.) If you try to use my flags, you will get an error.

Lastly, I used this challenge as a means to continue with my progress of learning Python. So please excuse the messy code and probably poor / old means of which I did some things. I'm sure there are much better ways to code various things I made, but I am still fairly new to Python.

Repository Information

You can read my full solution here: Full Solution

In order to solve the challenge, the main task you are given is to implement a virtual machine that can emulate the given opcodes found within the challenge arch-spec file. To handle this part of the challenge, and assisting with other parts, I wrote the virtual machine and a disassembler for the binary data file in Python.

Throughout the challenge, once the VM is functional, there are puzzles to be solved. The three puzzles all required their own implementation of code to be solved. Two of the puzzles I was able to solve in Python, however the other was too slow to implement in Python alone. Instead, I opt'd to use C++ for that one instead. (I made a Python implementation using ghetto threading, but it's ugly and slow so not worth including.)

The first puzzle is within the Ruins area of the game. My solver for that can be found here:

The next puzzle, which required the C++ implementation to not be ungodly slow, is for the teleporter item puzzle. That can be found here:

The final puzzle, in the Vault area, can be solved with my solution here:

Other files included in the repo are:

Challenge Information

== Synacor Challenge ==
In this challenge, your job is to use this architecture spec to create a
virtual machine capable of running the included binary.  Along the way,
you will find codes; submit these to the challenge website to track
your progress.  Good luck!


== architecture ==
- three storage regions
  - memory with 15-bit address space storing 16-bit values
  - eight registers
  - an unbounded stack which holds individual 16-bit values
- all numbers are unsigned integers 0..32767 (15-bit)
- all math is modulo 32768; 32758 + 15 => 5

== binary format ==
- each number is stored as a 16-bit little-endian pair (low byte, high byte)
- numbers 0..32767 mean a literal value
- numbers 32768..32775 instead mean registers 0..7
- numbers 32776..65535 are invalid
- programs are loaded into memory starting at address 0
- address 0 is the first 16-bit value, address 1 is the second 16-bit value, etc

== execution ==
- After an operation is executed, the next instruction to read is immediately after the last argument of the current operation.  If a jump was performed, the next operation is instead the exact destination of the jump.
- Encountering a register as an operation argument should be taken as reading from the register or setting into the register as appropriate.

== hints ==
- Start with operations 0, 19, and 21.
- Here's a code for the challenge website: fNCoeXxLEawt
- The program "9,32768,32769,4,19,32768" occupies six memory addresses and should:
  - Store into register 0 the sum of 4 and the value contained in register 1.
  - Output to the terminal the character with the ascii code contained in register 0.

== opcode listing ==
halt: 0
  stop execution and terminate the program
set: 1 a b
  set register <a> to the value of <b>
push: 2 a
  push <a> onto the stack
pop: 3 a
  remove the top element from the stack and write it into <a>; empty stack = error
eq: 4 a b c
  set <a> to 1 if <b> is equal to <c>; set it to 0 otherwise
gt: 5 a b c
  set <a> to 1 if <b> is greater than <c>; set it to 0 otherwise
jmp: 6 a
  jump to <a>
jt: 7 a b
  if <a> is nonzero, jump to <b>
jf: 8 a b
  if <a> is zero, jump to <b>
add: 9 a b c
  assign into <a> the sum of <b> and <c> (modulo 32768)
mult: 10 a b c
  store into <a> the product of <b> and <c> (modulo 32768)
mod: 11 a b c
  store into <a> the remainder of <b> divided by <c>
and: 12 a b c
  stores into <a> the bitwise and of <b> and <c>
or: 13 a b c
  stores into <a> the bitwise or of <b> and <c>
not: 14 a b
  stores 15-bit bitwise inverse of <b> in <a>
rmem: 15 a b
  read memory at address <b> and write it to <a>
wmem: 16 a b
  write the value from <b> into memory at address <a>
call: 17 a
  write the address of the next instruction to the stack and jump to <a>
ret: 18
  remove the top element from the stack and jump to it; empty stack = halt
out: 19 a
  write the character represented by ascii code <a> to the terminal
in: 20 a
  read a character from the terminal and write its ascii code to <a>; it can be assumed that once input starts, it will continue until a newline is encountered; this means that you can safely read whole lines from the keyboard and trust that they will be fully read
noop: 21
  no operation
Owner
:rainbow: Self-taught programmer / reverse engineer. Game hacker / modder. Looking for support for any of my projects? Check my homepage.
A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN

A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN Please follow Faster R-CNN and DAF to complete the environment confi

2 Jan 12, 2022
Social Fabric: Tubelet Compositions for Video Relation Detection

Social-Fabric Social Fabric: Tubelet Compositions for Video Relation Detection This repository contains the code and results for the following paper:

Shuo Chen 7 Aug 09, 2022
Model Serving Made Easy

The easiest way to build Machine Learning APIs BentoML makes moving trained ML models to production easy: Package models trained with any ML framework

BentoML 4.4k Jan 08, 2023
3ds-Ghidra-Scripts - Ghidra scripts to help with 3ds reverse engineering

3ds Ghidra Scripts These are ghidra scripts to help with 3ds reverse engineering

Zak 7 May 23, 2022
KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021

KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021

IELab@ Korea University 74 Dec 28, 2022
a reimplementation of Optical Flow Estimation using a Spatial Pyramid Network in PyTorch

pytorch-spynet This is a personal reimplementation of SPyNet [1] using PyTorch. Should you be making use of this work, please cite the paper according

Simon Niklaus 269 Jan 02, 2023
OoD Minimum Anomaly Score GAN - Code for the Paper 'OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary'

OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary Out-of-Distribution Minimum Anomaly Score GAN (OMASGAN) C

- 8 Sep 27, 2022
imbalanced-DL: Deep Imbalanced Learning in Python

imbalanced-DL: Deep Imbalanced Learning in Python Overview imbalanced-DL (imported as imbalanceddl) is a Python package designed to make deep imbalanc

NTUCSIE CLLab 19 Dec 28, 2022
HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval

HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval [toc] 1. Introduction This repository provides the code for our paper at

13 Dec 08, 2022
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.

counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code

Networks Learning 11 Dec 09, 2022
Shape-Adaptive Selection and Measurement for Oriented Object Detection

Source Code of AAAI22-2171 Introduction The source code includes training and inference procedures for the proposed method of the paper submitted to t

houliping 24 Nov 29, 2022
Pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks."

alpha-GAN Unofficial pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks." arXi

Victor Shepardson 78 Dec 08, 2022
PyTorch implementation of probabilistic deep forecast applied to air quality.

Probabilistic Deep Forecast PyTorch implementation of a paper, titled: Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Abdulmajid Murad 13 Nov 16, 2022
📖 Deep Attentional Guided Image Filtering

📖 Deep Attentional Guided Image Filtering [Paper] Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao ,Xiangyang Ji Harbin Institute of Technology,

9 Dec 23, 2022
The Simplest DCGAN Implementation

DCGAN in TensorLayer This is the TensorLayer implementation of Deep Convolutional Generative Adversarial Networks. Looking for Text to Image Synthesis

TensorLayer Community 310 Dec 13, 2022
Learning from Synthetic Shadows for Shadow Detection and Removal [Inoue+, IEEE TCSVT 2020].

Learning from Synthetic Shadows for Shadow Detection and Removal (IEEE TCSVT 2020) Overview This repo is for the paper "Learning from Synthetic Shadow

Naoto Inoue 67 Dec 28, 2022
Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

PyTorch implementation of BERT and PALs Introduction Work by Asa Cooper Stickland and Iain Murray, University of Edinburgh. Code for BERT and PALs; mo

Asa Cooper Stickland 70 Dec 29, 2022
Code repository for our paper "Learning to Generate Scene Graph from Natural Language Supervision" in ICCV 2021

Scene Graph Generation from Natural Language Supervision This repository includes the Pytorch code for our paper "Learning to Generate Scene Graph fro

Yiwu Zhong 64 Dec 24, 2022
Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continuous domains.

This repository is no longer maintained. Please use our new Softlearning package instead. Soft Actor-Critic Soft actor-critic is a deep reinforcement

Tuomas Haarnoja 752 Jan 07, 2023
LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference This repository contains PyTorch evaluation code, training code and pretrained

Facebook Research 504 Jan 02, 2023