  Blockchain and Cryptocurrencies are gaining unprecedented popularity and
understanding. Meanwhile, Ethereum is gaining a significant popularity in the
blockchain community, mainly due to the fact that it is designed in a way that
enables developers to write smart contract and decentralized applications
(Dapps). This new paradigm of applications opens the door to many possibilities
and opportunities. However, the security of Ethereum smart contracts has not
received much attention; several Ethereum smart contracts malfunctioning have
recently been reported. Unlike many previous works that have applied static and
dynamic analyses to find bugs in smart contracts, we do not attempt to define
and extract any features; instead we focus on reducing the expert's labor
costs. We first present a new in-depth analysis of potential attacks
methodology and then translate the bytecode of solidity into RGB color code.
After that, we transform them to a fixed-sized encoded image. Finally, the
encoded image is fed to convolutional neural network (CNN) for automatic
feature extraction and learning, detecting compiler bugs of Ethereum smart
contract.
