ARTIFICIAL INTELLIGENCE IN MEDIA – MAKING SECURITY SMARTER

Pay TV has evolved from a walled garden, set-top box, model to include online services. Although there are numerous operator and consumer benefits that result from this shift, it also opens up tremendous piracy threats that are a nightmare to control. The sheer volume of information being shared in a more open environment, means that manpower alone is insufficient to process and detect threats effectively. As Artificial Intelligence technology develops in the media space, its application in security must focus on more than closing gaps and locking down assets. Security threats must be spotted and managed faster and more efficiently, before a security instance even occurs. In this paper, we explain how to leverage Artificial Intelligence to fight piracy by using content monitoring solutions that search and identify pirated content on the internet. At the core of this technology is an AIpowered computer vision system that identifies the original source of distributed content based on the visual information present in the image (e.g. a broadcaster logo). We cover practical issues around building such a system, including its workflow, training and performance. INTRODUCTION Artificial Intelligence (AI) is becoming entrenched in our day-to-day lives. Due to rapid innovation over the last few years, AI is now capable of simulating a range of human brain functions, including pattern recognition. AI is disrupting a variety of industries, including the pay media industry, where it is being considered for implementation of complex security and anti-piracy schemes. In this paper, we present how AI can assist in fighting piracy. It is widely believed that the most significant threat to the content production and distribution businesses today is the illegal redistribution of content. Pirates recompress content that has been decrypted using a legitimate subscription and stream the content either online or via various streaming IPTV devices. The business model for these types of piracy includes advertising, hardware sales and subscriptions. A typical example of such illegal activity is near real-time redistribution of live sports events [10]. Such events have high value when they are live. Pirates publish links on social media and aggregation sites to illegal content streams. They re-broadcast the original content with limited modifications (Figure 1). Redistribution is done either via web streaming, with or without using a Content Delivery Network (CDN), or via Peer-to-Peer applications such as Sopcast or Acestream. This form of piracy has become widespread and it is hard to analyze which streams of content are decoys and which are actual pirated streams. One of the problems with the metadata that is provided along with the pirated content (if any), is that it is unstructured and inconsistent. This means that it is impossible to determine the actual content in a re-broadcast video stream without analyzing the stream. This is further complicated when multiple television channels are transmitting the same sports event. Even if the event has been identified as being a specific football match, the logo of the broadcaster is the only indication of the original source of the pirated content. The recognition of the logos in the video content is made even more difficult by the varying qualities of encoding applied to redistributed video. The reproduction quality of logos can be extremely poor, and logos can even be distorted or purposefully hidden. The media industry and anti-piracy companies have sought to address these issues by applying machine learning (ML). ML can be used to automatically process streams distributed by pirate aggregation sites or other distribution media, and recognize the original source of the video stream by identifying the broadcaster logo. Figure 1 – Example of captured streams, with distorted logos due to re-broadcasting. ROLE OF ARTIFICIAL INTELLIGENCE IN BROADCAST MEDIA Combating redistribution piracy requires the proactive search and identification of illegal re-broadcasts. AI plays a key role by enabling detection of illegal streams, through semantic analysis of social media advertisements and/or web page indexes and by enabling inspection of visual elements in the re-distributed content, matching it to the original source. Visual elements for identification can vary from the type of content (i.e. football match, movie, new show etc. [11]), the presence of broadcaster logos, detection of known people etc. [12]. Recent advances in ML have shown that neural networks can be designed and trained for various image recognition tasks with high accuracy. Neural networks are attractive because they can learn to distinguish patterns in data by only looking at the input data and the expected output, with no human intervention. CNNs are a sub-class of neural networks which, among other transformations, learn and apply convolutional filters on input data. CNNs are currently state-of-the art for many image processing tasks, significantly outperforming previous methods based on detecting manually crafted features [7]. The major barrier to effectively utilizing a CNN is the training process. This requires a large dataset that encompasses examples of all the possible classification outcomes, as well as large computational resources to train the networks using the training data. The dataset requires labelling of the data to indicate to which classification group each element of the data belongs to. For example, when training to recognize a specific logo, there needs to be thousands of images of the logo with different levels of degradation, images without the logo and images with other logos. Razvian et al [5] showed that CNNs demonstrate generic feature extraction capabilities when processing images. They showed that the same network architectures can be repurposed to many different visual recognition tasks, by only changing the training data. Additionally, they showed that a network trained for a task, such as detection of objects [7], can be partially re-trained with little data, for a different task, such as recognizing faces or recognizing alphanumeric characters. We build upon this knowledge to develop a system for recognizing logos. We have focused on practical issues in using ML to analyze video. We describe the complete workflow of the system for the use case of monitoring and detecting pirated live broadcasts. We show the design choices when selecting the architecture of the ML system for detecting and recognizing logos, which impact the amount and type of data required for training CNNs, and the performance in terms of accuracy. USE CASE: LARGE-SCALE MONITORING OF ILLEGAL SPORT RE-BROADCASTING Monitoring illegal re-broadcasting is a four-step process: Discovery of illegal re-broadcasts Gathering data from illegal re-broadcasts Analyzing data Taking measures against re-broadcasters. The process starts with discovery of links to illegal re-broadcasts. Advertisements for illegal re-broadcasts are typically found on social media and indexing websites. Embedded in these advertisements are URIs specifying the protocol used to deliver the web stream (e.g. web stream, P2P stream etc.). Next, the validity of the stream needs to be verified. This is done by accessing the stream, and gathering data from it. The access method differs per protocol. The data consist of the actual content that is being broadcasted, and identifiers of the source of the stream. The gathered data is then analyzed, to identify its original source. The range of tools for analysis can vary from forensic fingerprinting, watermarking to visual content inspection. Based on the analysis and the information gathered on the source of the stream, the system may act by taking measures such as automatic reporting to the re-broadcaster’s internet service provider. Further analysis may be performed to identify any forensic marks inserted into the content that enable the tracing of the original subscription used to access the content. The redistribution can be disrupted through terminating access to the service. Here, we focus on the third step: analysis of visually identifiable information. As identifiers, we use broadcaster logos, which are present in virtually all frames from the original broadcast. Their detection can be done independently, without integration with the content production and delivery system of the content distributors themselves. The system takes a frame from a broadcast stream as an input, and detects whether the image frame has a broadcaster logo, and then identifies the logo. We expect the system to work in a noisy environment, where images are of poor quality, with distorted and/or partially visible logos. LOGO DETECTION AND RECOGNITION Logo recognition is a common problem in areas such as marketing, brand tracking [1], document classification [2], piracy detection, etc. Depending on the application area, logo recognition can be divided into three sub-problems: detection, localization and recognition. The goal of logo detection is to decide whether an input sample, such as an image or a video sequence, contains a logo of interest. Logo localization further provides information where the logo is located within the input, most commonly by providing a bounding box around it. Finally, logo recognition or classification deals with deciding which exact logo is found within a sample. There is an abundance of related work on logo detection and recognition. Early work separates the tasks of describing features and matching features for classification. Feature descriptions can be completely customized [2], or well-known such as Scale Invariant Feature Transform (SIFT) [3,9], and Histogram of Oriented Gradients (HOG) [13]. Feature matching is then treated as an ML problem, with Bag of Words [4], Support Vector Machines or Neural Networks [13] used as classification algorithms. Newer work on logo detection and recognition relies on CNNs. With