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Brand Reputation Management with Computer Vision: How to Fight ad Fraud and ‘Malgorithms’

To this day, using website blacklists represents one of the most popular methods of preventing brand safety incidents related to ad placement. However, brands that rely on this tool alone run the risk of reducing their ad outreach and losing revenue.

The data shows that not everyone is stuck with conventional-yet-limited  methods of optimizing the list of targeted websites for ad placement. The number of marketers who implement computer vision software as a brand safety tool has grown from 12% to 21% in just a year. And there’s a reason for that. This tech goes beyond white- and blacklists, enabling brands and advertisers to speed up ad placement research, identify fraud patterns, and measure content relevance.

Read on to find out some other benefits you’ll get by using computer vision for your advertising campaigns.

Avoiding association with inappropriate content

The 2019 Global Brand Safety Survey shows that an ad appearing alongside offensive content negatively impacts the brand perception — according to 49% of respondents. How do you know your carefully calibrated ads won’t be placed next to adult, sensitive, or extremist content?

Try a powerful combination of computer vision and natural language processing techs to thoroughly analyze the platforms in question. Image recognition will assist you in scanning on-page pictures for competitor’s branding, nudity, violence, offensive gestures, etc. And NLP will control the content adjacency with a comprehensive text analysis — by identifying vulgar language, hate speech, and fake news.

And what if the context contains non-obvious, hidden threats like calls to violence? Rest assured that a computer vision-fueled solution won’t miss them. What’s more, such a system will be capable of reviewing multiple websites at remarkable speed, sparing you the need to do an analysis manually — all without compromising results accuracy.

Preventing ad placement fraud

If a publisher attempts to resize, replace, or block your ad, you may not notice the fraud until the impressions you paid for are served.

By leveraging computer vision for fraud prevention, you’ll be able to verify whether your ad was placed in a valid slot and in full size. You’ll also be notified about other related inconsistencies — around ad context, visibility, and more — and will have time to reconsider your agreements with the publisher before your ad campaign is in full swing.

Computer vision can also assist you in detecting fraudulent placements and ‘impression laundering’ — preventing you from placing ads on the platforms with irrelevant content or audiences. By recognizing and blacklisting untrustworthy publishers at an early stage, you’ll be able to protect your brand reputation before your dollars are spent.

Using scene-based video analysis

According to AdColony, the cases when an ad goes along inappropriate content are most common for crowded advertising platforms like Facebook and YouTube. Indeed, how do you manage to thoroughly analyze the context for your ad, when more than 500 hours of video content are uploaded every minute?

To make sure your brand never grabs the headlines after a failed ad campaign, apply a computer vision system set to define unsafe context in a video. Coupled with AI, such a solution will perform accurate detection and recognition of objects, faces, emotions, and activities, marking videos that are unsuitable for ad insertions.

From avoiding dubious messages around your ads to ensuring campaign transparency to increasing ad targeting accuracy, computer vision can improve the overall ad performance over time.

Tapping into actionable insights

The data you gather during your ad campaigns helps you understand what your brand reputation strategy lacks.

After this data undergoes an AI-powered analysis, you’ll get a deep dive into your brand safety level. Namely, you’ll understand the most profitable ad types, identify the websites that don’t align with your values, and assess possible brand reputation damage.

Empowered with these valuable insights, you’ll be able to change the focus of your ads, optimize your ad spendings, and fine-tune your digital advertising strategy. These insights will also enable you to adjust your ‘negative targets’ list and enhance decision-making around ad placement.

Unlock brand-safe advertising

If you want to minimize reputational risks along the way, opt for computer vision.

In the long run, this technology will help you not only automate your ad placement validation and video context analysis, but also develop solid brand safety management competence.

Author’s bio:

 Oksana Mikhalchuk is a Technology Writer at Oxagile, a New York-based provider of next-gen software engineering solutions around IoT, AI, computer vision, biometrics, and more. Oksana creates content about state-of-art tech opportunities in healthcare, education, entertainment, and manufacturing. You can reach Oksana at or LinkedIn.

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