6 accepted papers
Securing personal identity against deepfake attacks is increasingly critical in the digital age, especially for celebrities and political figures whose faces are easily accessible and frequently targeted. Most existing deepfake detection methods focus on general-purpose scenarios and often ignore th…
Image forgery detection and localization (IFDL) is of vital importance as forged images can spread misinformation that poses potential threats to our daily life. However, previous methods still struggled to effectively handle forged images processed with diverse forgery operations in real-world scen…
The proliferation of deepfake faces poses huge potential negative impacts on our daily lives. Despite substantial advancements in deepfake detection over these years, the generalizability of existing methods against forgeries from unseen datasets or created by emerging generative models remains cons…
As manipulating images may lead to misinterpretation of the visual content addressing the image forgery detection and localization (IFDL) problem has drawn serious public concerns. In this work we propose a simple assumption that the effective forensic method should focus on the mesoscopic propertie…
Most previous deepfake detection methods bent their efforts to discriminate artifacts by end-to-end training. However, the learned networks often fail to mine the generic face forgery information efficiently due to ignoring data diversity. In this work, we propose to introduce sample hardness into t…
In this paper, we present the Screenshot Text Forgery Dataset (STFD), which is the first public dataset for the smartphone screenshot text forgery localization task. To address such a task, we propose a novel Screenshot Text Forgery Localization Network (STFL-Net). Specifically, we introduce the OCR…