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Digital watermarking technology is primarily the joining of the rightful owner of the protected media. Once the media are suspected to be illegally used, an open algorithm can be used to extract the digital watermark for the purpose of showing the media's ownership. From a hidden watermark in the media from the appearance point of view, general digital watermarking technologies can be divided into two categories: visible watermark technology and invisible watermark technology. Visual watermark technology embeds a watermark into the protected media to declare ownership and deter pirate behavior. In this paper, we propose a reversible visible watermark method, which embeds a binary-imaged watermark into gray-scale images to create a visible watermark. Not using complex calculations, this paper tries to simply change the pixel value to achieve the digital watermark, where our scheme is also against the possible detections with LSB-based manners in use. Furthermore, a reversible steganographic method is used to embed the watermarking information into the watermarking images. The watermark information can then be used to recover the original images.

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Coinhive released its browser-based cryptocurrency mining code in September 2017, and vicious web page writers, called vicious miners hereafter, began to embed mining JavaScript code into their web pages, called mining pages hereafter. As a result, browser users surfing these web pages will benefit mine cryptocurrencies unwittingly for the vicious miners using the CPU resources of their devices. The above activity, called Cryptojacking, has become one of the most common threats to web browser users. As mining pages influence the execution efficiency of regular programs and increase the electricity bills of victims, security specialists start to provide methods to block mining pages. Nowadays, using a blocklist to filter out mining scripts is the most common solution to this problem. However, when the number of new mining pages increases quickly, and vicious miners apply obfuscation and encryption to bypass detection, the detection accuracy of blacklist-based or feature-based solutions decreases significantly. This paper proposes a solution, called MinerGuard, to detect mining pages. MinerGuard was designed based on the observation that mining JavaScript code consumes a lot of CPU resources because it needs to execute plenty of computation. MinerGuard does not need to update data used for detection frequently. On the contrary, blacklist-based or feature-based solutions must update their blocklists frequently. Experimental results show that MinerGuard is more accurate than blacklist-based or feature-based solutions in mining page detection. MinerGuard’s detection rate for mining pages is 96%, but MinerBlock, a blacklist-based solution, is 42.85%. Moreover, MinerGuard can detect 0-day mining pages and scripts, but the blacklist-based and feature-based solutions cannot.