Broadsign and Clear Channel: digital partners in out-of-home
Broadsign is a global company providing software for outdoor advertising, or “out-of-home,” networks. “We work with almost all of the world's largest out-of-home networks and not just on the digital side, but also paper-based out-of-home networks,” says Maarten Dollevoet, Chief Revenue Officer at Broadsign. “Clients use the Broadsign platform for anything from ad serving to network operations, and also to manage and optimise their sales workflows - as well as connecting to new channels like programmatic buying.” Broadsign is one of the only platforms with such a wide spectrum of offerings, but it also allows companies to do more with less. “One of the key benefits of working with Broadsign is that we focus a lot on automation and optimisation, allowing customers to devote more time to their high-value activities rather than more routine and repetitive tasks.”
The out-of-home industry has experienced considerable growth on the digital side, with market leader Broadsign being ideally placed to influence trends. One is digitisation, the evolution from posters and printed billboards to digital signs. “With the move to digital, advertisers are able to leverage its inherent flexibility to react in real time to content changes, audience movement, as well as the ability to use data for improved audience targeting and to dynamically change screen content,” says Dollevoet. “Content is King, but context is everything. While we aren’t close to the Tom Cruise-style, Minority Report-type of targeting, we can deliver the right message to the right audience at the right time.” One example was a recent campaign for takeout food, where the content automatically displayed a ‘pick-up’ message when the sun was shining, versus promoting the ‘home delivery’ option when it was raining.
Another important trend is the rise of programmatic transactions, where advertising is bought and sold in real time via automated bidding systems, much like it is today for online and mobile ads. “We see programmatic not only as a way to automate the buying process, but also an opportunity to connect with non-traditional out-of-home advertisers who may benefit from more omnichannel media campaigns.”
When Clear Channel International was looking for a best-in-class content management system to support its digital transformation, it chose Broadsign and its offering. “The relationship has really flourished and has become a strong partnership. After the CMS, the team at Clear Channel wanted to leverage more of the platform to help them scale other parts of their business. It added Broadsign Direct, a tool to help them scale their sales organisation so salespeople were able to respond to customer requests and RFPs quicker, and sell more of their network at a premium. So what started out as an initial relationship on the CMS side has become a true partnership for the rest of the business.”
While the ongoing COVID-19 pandemic has had an undoubted impact on out-of-home advertising, changes in the online advertising space have revealed fresh opportunities. “With the impact of what's happening in the online world with privacy concerns and the disappearance of the cookie, we actually believe the momentum for out-of-home is stronger than ever.” Such trends lead Dollevoet to believe the future is bright for the industry. “COVID-19 has accelerated the adoption of programmatic too. If we look at our systems, we see a V-shaped recovery of programmatic because advertisers who have smaller budgets are looking for more flexibility, and the ability to turn budgets on and off more granularly or in a more targeted manner.”
Facebook Develops AI to Crackdown on Deepfakes
In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale?
Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.”
The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with.
Why Reverse Engineering?
Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient.
First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source.
How It Works
Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.
To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters.
What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’.
What Did They Find?
‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising.
Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.