A Unified Approach of Detecting Misleading Images via Tracing its Instances on Web and Analysing its Past Context for the Verification of Content

TL;DR

This paper presents a unified system for verifying the credibility of images on social media by tracing their instances online and analyzing their past context, using machine learning and deep learning techniques.

Contribution

It introduces a generalized approach combining image tracing and context analysis for automatic misleading image detection, evaluated on real-world Twitter data.

Findings

Deep learning (Bi-directional LSTM) improves verification accuracy.

Microsoft Bing image search outperforms Google in retrieving relevant titles.

Using multimedia clues is more effective than analyzing posted content alone.

Abstract

The verification of multimedia content over social media is one of the challenging and crucial issues in the current scenario and gaining prominence in an age where user-generated content and online social web platforms are the leading sources in shaping and propagating news stories. As these sources allow users to share their opinions without restriction, opportunistic users often post misleading/ unreliable content on social media such as Twitter, Facebook, etc. At present, to lure users towards the news story, the text is often attached with some multimedia content (images/videos/audios). Verifying these contents to maintain the credibility and reliability of social media information is of paramount importance. Motivated by this, we proposed a generalized system that supports the automatic classification of images into credible or misleading. In this paper, we investigated machine learning-based as well as deep learning-based approaches utilized to verify misleading multimedia content, where the available image traces are used to identify the credibility of the content. The experiment is performed on the real-world dataset (Media-eval-2015 dataset) collected from Twitter. It also demonstrates the efficiency of our proposed approach and features using both Machine and Deep Learning Model (Bi-directional LSTM). The experiment result reveals that the Microsoft bings image search engine is quite effective in retrieving titles and performs better than our study's Google image search engine. It also shows that gathering clues from attached multimedia content (image) is more effective than detecting only posted content-based features.