The popularity of social networks as primary mediums for sharing visual content has made it crucial for forensic experts to identify the original platform of multimedia content. Various methods address this challenge, but the constant emergence of new platforms and updates to existing ones often render forensic tools ineffective shortly after release. This necessitates the regular updating of methods and models, which can be particularly cumbersome for techniques based on neural networks which cannot quickly adapt to new classes without sacrificing performance on previously learned ones – a phenomenon known as . Recently, researchers aimed at mitigating this problem via a family of techniques known as . In this paper we study the applicability of continual learning techniques to the social network identification task by evaluating two relevant forensic scenarios: , for handling newly introduced social media platforms, and , for addressing updated versions of a set of existing social networks. We perform an extensive experimental evaluation of a variety of continual learning approaches applied to these two scenarios. Experimental results demonstrate that, although Continual Social Network Identification remains a difficult problem, catastrophic forgetting can be significantly mitigated in both scenarios by retaining only a fraction of the image patches from past task training samples or by employing previous tasks prototypes.

中文翻译：

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自适应社交网络识别的持续学习

社交网络作为共享视觉内容的主要媒介的流行使得法医专家识别多媒体内容的原始平台变得至关重要。有多种方法可以解决这一挑战，但新平台的不断出现和现有平台的更新往往会使取证工具在发布后不久就失效。这就需要定期更新方法和模型，这对于基于神经网络的技术来说尤其麻烦，因为神经网络无法快速适应新类别而不牺牲先前学习的类别的性能 - 这种现象称为 。最近，研究人员旨在通过一系列称为 的技术来缓解这个问题。在本文中，我们通过评估两个相关的取证场景来研究持续学习技术在社交网络识别任务中的适用性：用于处理新引入的​​社交媒体平台，以及用于解决一组现有社交网络的更新版本。我们对应用于这两种场景的各种持续学习方法进行了广泛的实验评估。实验结果表明，尽管持续社交网络识别仍然是一个难题，但通过仅保留过去任务训练样本中的一小部分图像补丁或使用以前的任务原型，在这两种情况下都可以显着减轻灾难性遗忘。