Ethics begins where entitlement ends.
Do what’s right — not just what’s allowed.
#EthicsMatter #RightVsRight #MoralCompass #TruthLens #NewsPerspective #ValueSystem #IntegrityFirst #EthicalLiving #DailyWisdom #ThoughtOfTheDay #RealTalkDaily #HardTruths #StayAware #CharacterCounts #LifeLessons
Do what’s right — not just what’s allowed.
#EthicsMatter #RightVsRight #MoralCompass #TruthLens #NewsPerspective #ValueSystem #IntegrityFirst #EthicalLiving #DailyWisdom #ThoughtOfTheDay #RealTalkDaily #HardTruths #StayAware #CharacterCounts #LifeLessons
December 4, 2025 at 5:01 AM
Ethics begins where entitlement ends.
Do what’s right — not just what’s allowed.
#EthicsMatter #RightVsRight #MoralCompass #TruthLens #NewsPerspective #ValueSystem #IntegrityFirst #EthicalLiving #DailyWisdom #ThoughtOfTheDay #RealTalkDaily #HardTruths #StayAware #CharacterCounts #LifeLessons
Do what’s right — not just what’s allowed.
#EthicsMatter #RightVsRight #MoralCompass #TruthLens #NewsPerspective #ValueSystem #IntegrityFirst #EthicalLiving #DailyWisdom #ThoughtOfTheDay #RealTalkDaily #HardTruths #StayAware #CharacterCounts #LifeLessons
reasoning capabilities of large language models (LLMs) like GPT-4 to analyze and aggregate evidence into informed decisions. By adopting a multimodal approach, TruthLens seamlessly integrates visual and semantic reasoning to not only classify images [4/7 of https://arxiv.org/abs/2503.15342v1]
March 20, 2025 at 6:08 AM
reasoning capabilities of large language models (LLMs) like GPT-4 to analyze and aggregate evidence into informed decisions. By adopting a multimodal approach, TruthLens seamlessly integrates visual and semantic reasoning to not only classify images [4/7 of https://arxiv.org/abs/2503.15342v1]
Yanqi Wu, Runhe Lai, Xinhua Lu, Qichao Chen, Zhiping Zhou, Jia-Xin Zhuang, Weijiang Yu, Ruixuan Wang: TruthLens: Object Hallucination Detection via Self-Evaluating Truthfulness Scores in LVLMs https://arxiv.org/abs/2608.05616 https://arxiv.org/pdf/2608.05616 https://arxiv.org/html/2608.05616
August 7, 2026 at 6:40 AM
Yanqi Wu, Runhe Lai, Xinhua Lu, Qichao Chen, Zhiping Zhou, Jia-Xin Zhuang, Weijiang Yu, Ruixuan Wang: TruthLens: Object Hallucination Detection via Self-Evaluating Truthfulness Scores in LVLMs https://arxiv.org/abs/2608.05616 https://arxiv.org/pdf/2608.05616 https://arxiv.org/html/2608.05616
DINOv2. This hybrid design leverages the complementary strengths of both models, enabling robust detection of subtle manipulations while maintaining interpretability. Extensive experiments on diverse datasets demonstrate that TruthLens outperforms [5/6 of https://arxiv.org/abs/2503.15867v1]
March 21, 2025 at 5:59 AM
DINOv2. This hybrid design leverages the complementary strengths of both models, enabling robust detection of subtle manipulations while maintaining interpretability. Extensive experiments on diverse datasets demonstrate that TruthLens outperforms [5/6 of https://arxiv.org/abs/2503.15867v1]
the eyes/nose/mouth look real or fake?"
The architecture of TruthLens combines the global contextual understanding of multimodal large language models like PaliGemma2 with the localized feature extraction capabilities of vision-only models like [4/6 of https://arxiv.org/abs/2503.15867v1]
The architecture of TruthLens combines the global contextual understanding of multimodal large language models like PaliGemma2 with the localized feature extraction capabilities of vision-only models like [4/6 of https://arxiv.org/abs/2503.15867v1]
March 21, 2025 at 5:59 AM
the eyes/nose/mouth look real or fake?"
The architecture of TruthLens combines the global contextual understanding of multimodal large language models like PaliGemma2 with the localized feature extraction capabilities of vision-only models like [4/6 of https://arxiv.org/abs/2503.15867v1]
The architecture of TruthLens combines the global contextual understanding of multimodal large language models like PaliGemma2 with the localized feature extraction capabilities of vision-only models like [4/6 of https://arxiv.org/abs/2503.15867v1]
real or fake but also provides detailed textual reasoning for its predictions. Unlike traditional methods, TruthLens effectively handles both face-manipulated DeepFakes and fully AI-generated content while addressing fine-grained queries such as "Does [3/6 of https://arxiv.org/abs/2503.15867v1]
March 21, 2025 at 5:59 AM
real or fake but also provides detailed textual reasoning for its predictions. Unlike traditional methods, TruthLens effectively handles both face-manipulated DeepFakes and fully AI-generated content while addressing fine-grained queries such as "Does [3/6 of https://arxiv.org/abs/2503.15867v1]
are often limited to binary classification (real vs. fake) and lack interpretability. To address these challenges, we propose TruthLens, a novel and highly generalizable framework for DeepFake detection that not only determines whether an image is [2/6 of https://arxiv.org/abs/2503.15867v1]
March 21, 2025 at 5:59 AM
are often limited to binary classification (real vs. fake) and lack interpretability. To address these challenges, we propose TruthLens, a novel and highly generalizable framework for DeepFake detection that not only determines whether an image is [2/6 of https://arxiv.org/abs/2503.15867v1]
Rohit Kundu, Athula Balachandran, Amit K. Roy-Chowdhury: TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data https://arxiv.org/abs/2503.15867 https://arxiv.org/pdf/2503.15867 https://arxiv.org/html/2503.15867
March 21, 2025 at 5:59 AM
Rohit Kundu, Athula Balachandran, Amit K. Roy-Chowdhury: TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data https://arxiv.org/abs/2503.15867 https://arxiv.org/pdf/2503.15867 https://arxiv.org/html/2503.15867
outperforms conventional methods, achieving high accuracy on challenging datasets while maintaining a strong emphasis on explainability. By reframing deepfake detection as a reasoning-driven process, TruthLens establishes a new paradigm in combating [6/7 of https://arxiv.org/abs/2503.15342v1]
March 20, 2025 at 6:08 AM
outperforms conventional methods, achieving high accuracy on challenging datasets while maintaining a strong emphasis on explainability. By reframing deepfake detection as a reasoning-driven process, TruthLens establishes a new paradigm in combating [6/7 of https://arxiv.org/abs/2503.15342v1]
as real or fake but also provide interpretable explanations for its decisions. This transparency enhances trust and provides valuable insights into the artifacts that signal synthetic content. Extensive evaluations demonstrate that TruthLens [5/7 of https://arxiv.org/abs/2503.15342v1]
March 20, 2025 at 6:08 AM
as real or fake but also provide interpretable explanations for its decisions. This transparency enhances trust and provides valuable insights into the artifacts that signal synthetic content. Extensive evaluations demonstrate that TruthLens [5/7 of https://arxiv.org/abs/2503.15342v1]
training-free framework that reimagines deepfake detection as a visual question-answering (VQA) task. TruthLens utilizes state-of-the-art large vision-language models (LVLMs) to observe and describe visual artifacts and combines this with the [3/7 of https://arxiv.org/abs/2503.15342v1]
March 20, 2025 at 6:08 AM
training-free framework that reimagines deepfake detection as a visual question-answering (VQA) task. TruthLens utilizes state-of-the-art large vision-language models (LVLMs) to observe and describe visual artifacts and combines this with the [3/7 of https://arxiv.org/abs/2503.15342v1]
predominantly rely on binary classification models that focus on accuracy while often neglecting interpretability, leaving users without clear insights into why an image is deemed real or fake. To bridge this gap, we introduce TruthLens, a novel [2/7 of https://arxiv.org/abs/2503.15342v1]
March 20, 2025 at 6:08 AM
predominantly rely on binary classification models that focus on accuracy while often neglecting interpretability, leaving users without clear insights into why an image is deemed real or fake. To bridge this gap, we introduce TruthLens, a novel [2/7 of https://arxiv.org/abs/2503.15342v1]
I just scored 10 / 10 on truthlens round 13 by @SuccinctLabs! #SuccinctStars
May 7, 2025 at 11:31 AM
I just scored 10 / 10 on truthlens round 13 by @SuccinctLabs! #SuccinctStars
I just scored 10 / 10 on truthlens round 12 by @SuccinctLabs! #SuccinctStars
April 30, 2025 at 7:05 AM
I just scored 10 / 10 on truthlens round 12 by @SuccinctLabs! #SuccinctStars
I just scored 10 / 10 on truthlens round 11 by @SuccinctLabs! #SuccinctStars
April 26, 2025 at 1:20 PM
I just scored 10 / 10 on truthlens round 11 by @SuccinctLabs! #SuccinctStars
I just scored 10 / 10 on truthlens round 10 by @SuccinctLabs! #SuccinctStars
April 16, 2025 at 10:25 AM
I just scored 10 / 10 on truthlens round 10 by @SuccinctLabs! #SuccinctStars
I just scored 10 / 10 on truthlens round 9 by @SuccinctLabs! #SuccinctStars
April 10, 2025 at 11:36 AM
I just scored 10 / 10 on truthlens round 9 by @SuccinctLabs! #SuccinctStars
I just scored 10 / 10 on truthlens round 8 by @SuccinctLabs! #SuccinctStars
April 4, 2025 at 9:20 AM
I just scored 10 / 10 on truthlens round 8 by @SuccinctLabs! #SuccinctStars
I just scored 9 / 10 on truthlens round 7 by @SuccinctLabs! #SuccinctStars
March 25, 2025 at 9:49 PM
I just scored 9 / 10 on truthlens round 7 by @SuccinctLabs! #SuccinctStars
I just scored 10 / 10 on truthlens round 6 by @SuccinctLabs! #SuccinctStars
March 19, 2025 at 6:09 AM
I just scored 10 / 10 on truthlens round 6 by @SuccinctLabs! #SuccinctStars
I just scored 7 / 10 on truthlens round 5 by @SuccinctLabs! #SuccinctStars
March 11, 2025 at 11:55 PM
I just scored 7 / 10 on truthlens round 5 by @SuccinctLabs! #SuccinctStars
Rohit Kundu, Athula Balachandran, Amit K. Roy-Chowdhury
TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data
https://arxiv.org/abs/2503.15867
TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data
https://arxiv.org/abs/2503.15867
March 21, 2025 at 8:07 AM
Rohit Kundu, Athula Balachandran, Amit K. Roy-Chowdhury
TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data
https://arxiv.org/abs/2503.15867
TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data
https://arxiv.org/abs/2503.15867