AI Image Detection: More Robust Than You Think

I keep seeing posts claiming: “AI image detection completely fails in the real world. JPEG compression and resizing destroy all detection signals. These methods only work in labs with pristine images.”

This narrative is everywhere. But is it actually true?

Major Research Says Detection Works (In Theory)

Recent papers show promising results:

• Detecting Diffusion Models (CVPR 2023) - Frequency analysis detects diffusion-generated images with >90% accuracy
• GenImage Benchmark (2023) - Multi-generator dataset showing detectors work across Stable Diffusion, DALL-E, Midjourney
• LGrad (CVPR 2023) - Gradient-based features generalize across GAN architectures
• Preprocessing Detection (Gragnaniello et al.) - Shows FFT can detect synthetic images despite compression

The problem: Most papers test on pristine images. When they mention “robustness,” they often test on different datasets, not degraded conditions.

Why I Built This Myself

I wanted to answer a simple question: Do these methods actually survive JPEG compression and social media resizing?

Not just theoretically—I wanted to see it with real numbers, real degradation, and code anyone could run. Most papers don’t show what happens when you apply JPEG Q=75 (typical web quality) or resize to half-resolution (typical social media).

So I built an experiment to test three popular detection methods—gradient features, frequency analysis, and a simple CNN—under realistic conditions. The results surprised me.

Detection methods are significantly more robust than the skeptics claim.

This post cuts through the hype with experiments and code. We’ll implement three detection approaches, test them on real data, and see what actually survives real-world conditions like JPEG compression and resizing.

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The Claim: Camera Physics vs AI Generation

The argument goes like this:

Real photos pass through a physical pipeline:

• Light hits a sensor (introducing PRNU noise)
• Demosaicing reconstructs RGB from Bayer pattern
• In-camera processing and JPEG compression
• Result: consistent gradient patterns from real optics

graph TB
    subgraph "Real Photo Pipeline"
        A1[Light] --> B1[Lens/Optics]
        B1 --> C1[Image Sensor]
        C1 --> D1[Demosaicing]
        D1 --> E1[Processing]
        E1 --> F1[JPEG Compression]
        F1 --> G1[Real Image]
    end
    style G1 fill:#6c6,stroke:#333,stroke-width:2px

AI images are generated from noise:

• Diffusion models denoise latent representations
• No physical sensor, no lens aberrations
• Gradients are “visually plausible but statistically different”

graph TB
    subgraph "AI Generation Pipeline"
        A2[Random Noise] --> B2[Diffusion Model]
        B2 --> C2[Denoising Steps]
        C2 --> D2[Latent Decoding]
        D2 --> E2[Upsampling]
        E2 --> G2[AI Image]
    end
    
    style G2 fill:#f96,stroke:#333,stroke-width:2px

The Detection Hypothesis

Why detection should work:

Gradient-based features - Real cameras introduce specific gradient patterns from optics, sensor noise (PRNU), and demosaicing. AI models generate images from noise without these physical constraints.

Frequency domain analysis - Diffusion models tend to have lower high-frequency content than real images (too smooth), while GANs have higher high-frequency content (oversharpening). The FFT spectrum reveals these differences.

Deep learning - CNNs can learn subtle statistical patterns that hand-crafted features miss, potentially adapting to degraded conditions.

The gap between lab and reality: Most research tests on pristine images or different datasets. Few studies rigorously test what happens when you apply typical web/social media degradation to the same images. This is what we’re testing here.

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The Experiment: Three Detection Approaches

We’ll test three methods on CIFAKE, a dataset of 60k real images (CIFAR-10) vs 60k AI images (Stable Diffusion v1.4):

1. Gradient Features + PCA: Extract Sobel gradients, compute statistics, visualize with PCA
2. Frequency Domain (FFT): Analyze FFT magnitude spectrum differences
3. Simple CNN Detector: Train a basic classifier

Then we’ll test robustness by applying JPEG compression and resizing to both classes equally.

Visual Evidence: What Gradients Actually Look Like

Before diving into the code, let’s see what gradient features reveal:

Real vs AI-generated images with their gradient magnitude, horizontal (X), and vertical (Y) components. Notice the subtle differences in gradient patterns.

The gradient magnitude heatmaps show where edges and textures are strongest. Real photos from camera sensors tend to have slightly different gradient distributions than diffusion-generated images, but the differences are subtle and easily destroyed by post-processing.

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The Code: Fully Functional PyTorch Implementation

The complete implementation is available on GitHub:

github.com/gsantopaolo/synthetic-image-detection

Clone and run:

git clone https://github.com/gsantopaolo/synthetic-image-detection
cd synthetic-image-detection
pip install -r requirements.txt
python train.py

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1. Dataset with Degradation Pipeline

The core of the experiment is testing robustness. Here’s how we apply JPEG compression and resizing on-the-fly:

class CIFAKEDataset(Dataset):
    """CIFAKE dataset: Real CIFAR-10 images vs Stable Diffusion fakes"""
    
    def __init__(self, split='train', limit=None, seed=42, 
                 jpeg_quality=None, resize_factor=1.0):
        self.jpeg_quality = jpeg_quality
        self.resize_factor = resize_factor
        
        # Load from HuggingFace, sample if needed
        ds = load_dataset("dragonintelligence/CIFAKE-image-dataset", split=split)
        # ... sampling logic ...
        
    def __getitem__(self, idx):
        img = self.data[idx]
        
        # THIS IS THE KEY: Apply degradation on-the-fly
        if self.jpeg_quality:
            img = self._jpeg_compress(img, self.jpeg_quality)
        if self.resize_factor != 1.0:
            img = self._resize_and_restore(img, self.resize_factor)
        
        return torch.tensor(img), self.labels[idx]
    
    @staticmethod
    def _jpeg_compress(img, quality):
        """Compress image to JPEG at specified quality"""
        buf = io.BytesIO()
        img.save(buf, format='JPEG', quality=quality)
        buf.seek(0)
        return Image.open(buf).convert('RGB')
    
    @staticmethod
    def _resize_and_restore(img, factor):
        """Downscale then upscale back (simulates social media)"""
        w, h = img.size
        small = img.resize((int(w*factor), int(h*factor)), Image.BICUBIC)
        return small.resize((w, h), Image.BICUBIC)

Why this matters: By applying degradations in __getitem__, we can test the same dataset under different conditions without duplicating data. The _jpeg_compress and _resize_and_restore methods simulate real-world image processing pipelines.

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2. Gradient Feature Extraction

Here’s how we compute gradient-based features using Sobel filters:

def extract_gradient_features(images: torch.Tensor) -> np.ndarray:
    """Extract gradient statistics from images"""
    B, _, H, W = images.shape
    
    # Convert to grayscale
    r, g, b = images[:, 0:1], images[:, 1:2], images[:, 2:3]
    luma = 0.2126 * r + 0.7152 * g + 0.0722 * b
    
    # Sobel filters for edge detection
    sobel_x = torch.tensor([[-1, 0, 1], 
                            [-2, 0, 2], 
                            [-1, 0, 1]], dtype=torch.float32) / 8.0
    sobel_y = torch.tensor([[-1, -2, -1], 
                            [ 0,  0,  0], 
                            [ 1,  2,  1]], dtype=torch.float32) / 8.0
    
    # Apply convolution to get gradients
    gx = F.conv2d(luma, sobel_x.view(1, 1, 3, 3), padding=1)
    gy = F.conv2d(luma, sobel_y.view(1, 1, 3, 3), padding=1)
    
    # Compute gradient magnitude
    mag = torch.sqrt(gx**2 + gy**2)
    
    # Extract statistical features:
    # - Mean magnitude
    # - Covariance matrix (gradient structure tensor)
    # - Gradient histogram (distribution)
    # ... feature computation ...
    
    return features.detach().cpu().numpy()

The features: We extract 14 features per image: mean gradient magnitude, covariance statistics (trace, determinant), and an 8-bin histogram of gradient magnitudes. These capture edge strength and texture patterns.

---

3. Frequency Domain (FFT) Features

For frequency analysis, we compute radial energy profiles:

def extract_fft_features(images: torch.Tensor) -> np.ndarray:
    """Extract frequency-domain features using FFT"""
    B, _, H, W = images.shape
    
    # Convert to grayscale
    gray = 0.2126 * images[:,0] + 0.7152 * images[:,1] + 0.0722 * images[:,2]
    
    features = []
    for i in range(B):
        # 2D FFT and shift to center
        f = np.fft.fft2(gray[i].cpu().numpy())
        fshift = np.fft.fftshift(f)
        magnitude = np.abs(fshift)
        
        # Compute radial frequency profile
        # Split spectrum into 8 concentric rings
        # Measure energy in each ring (low freq → high freq)
        center_h, center_w = H // 2, W // 2
        y, x = np.ogrid[:H, :W]
        radius = np.sqrt((x - center_w)**2 + (y - center_h)**2)
        
        radial_profile = []
        for ring in range(8):
            mask = (radius >= ring/8 * max_r) & (radius < (ring+1)/8 * max_r)
            energy = np.mean(magnitude[mask])
            radial_profile.append(energy)
        
        # Add high-frequency ratio
        hf_ratio = high_freq_energy / low_freq_energy
        features.append(radial_profile + [hf_ratio])
    
    return np.array(features)

The features: 9 features per image - energy in 8 frequency bands (from DC/low to high) plus high-frequency ratio. This captures the frequency signature differences shown in our earlier plots.

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4. The CNN Baseline

For comparison, we also train a simple CNN (3 conv layers, batch norm, dropout). Standard architecture: Conv → ReLU → Pool → ... → Linear. Trained with Adam optimizer and BCE loss. Nothing fancy—just a baseline to see if learned features outperform hand-crafted ones.

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5. Putting It All Together: The Experiment Loop

Here’s the core experiment structure:

def main():
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    output_dir = Path('detection_results')
    
    # Define test scenarios
    scenarios = {
        'Raw Images': {'jpeg_quality': None, 'resize_factor': 1.0},
        'JPEG Q=75': {'jpeg_quality': 75, 'resize_factor': 1.0},
        'Resized (0.5x)': {'jpeg_quality': None, 'resize_factor': 0.5},
    }
    
    results = {}
    
    for scenario_name, params in scenarios.items():
        # Load dataset with degradation parameters
        train_ds = CIFAKEDataset('train', limit=5000, **params)
        val_ds = CIFAKEDataset('test', limit=2000, **params)
        
        # Extract gradient and FFT features
        gradient_feats = extract_gradient_features(val_images)
        fft_feats = extract_fft_features(val_images)
        
        # Evaluate classical detectors (Logistic Regression on features)
        grad_auc = evaluate_detector(gradient_feats, labels, "Gradient")
        fft_auc = evaluate_detector(fft_feats, labels, "FFT")
        
        # Train and evaluate CNN
        cnn_model = train_cnn(train_loader, val_loader, device, epochs=5)
        cnn_auc = evaluate_cnn(cnn_model, val_loader)
        
        results[scenario_name] = {
            'Gradient': grad_auc,
            'FFT': fft_auc,
            'CNN': cnn_auc
        }
    
    # Generate all plots
    plot_performance_degradation(results, output_dir)
    plot_pca_comparison(results, output_dir)
    plot_sample_images_with_gradients(val_ds, output_dir)
    plot_fft_spectra(val_ds, output_dir)
    plot_radial_frequency_profile(val_ds, output_dir)

The key insight: By parameterizing the dataset with jpeg_quality and resize_factor, we run the exact same experiment pipeline three times with different degradations. This reveals how performance changes under real-world conditions.

For the complete implementation including visualization functions, see the full code on GitHub.

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Running the Experiment

Clone the repository and run:

git clone https://github.com/gsantopaolo/synthetic-image-detection
cd synthetic-image-detection
conda create -n "syntetic-image" python=3.11.7  
conda activate syntetic-image  
pip install -r requirements.txt
python train.py

This trains the models and saves them to the models/ directory (~64 KB total). Training takes 15-30 minutes on CPU.

Detecting AI Images with detector.py

Once models are trained, you can detect AI images using the detector.py inference script:

# Detect a single image (ensemble mode - combines all 3 methods)
python detector.py photo.jpg
# Output: photo.jpg  Real: 23.0% | AI: 77.0% → 🤖 AI
#           (Gradient: 65.0%, FFT: 82.0%, CNN: 88.0%)

# Process an entire folder
python detector.py my_images/

# JSON output for automation/APIs
python detector.py photo.jpg --format json
# Output: {"file": "photo.jpg", "predictions": {...}, "verdict": "AI"}

# Use specific detector
python detector.py photo.jpg --model cnn        # CNN only (85-97% AUC)
python detector.py photo.jpg --model gradient   # Gradient only (70-75% AUC)

# Custom threshold (higher = stricter)
python detector.py photo.jpg --threshold 0.7

How Ensemble Works

By default, the detector combines all three methods with weighted averaging:

Ensemble = 20% × Gradient + 30% × FFT + 50% × CNN

This leverages the CNN’s superior accuracy (85-97% AUC) while using hand-crafted features as validators. The ensemble approach is more robust than any single method, especially under degradation.

Output Formats

• --format simple (default): Human-readable with emojis (🤖 for AI, 📷 for Real)
• --format json: Machine-readable for automation, APIs, or batch processing

The detector works on any image size—the script automatically resizes to 32×32 for processing. For more usage examples and options, see the README.

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Training Output

Expected output from python train.py:

🔧 Using device: cuda
📥 Loading CIFAKE train split...
✅ Loaded 5000 images (2500 real, 2500 fake)

============================================================
Testing: Raw Images
============================================================
📊 Extracting features...

Gradient Features:
  Accuracy: 0.8542 ± 0.0123
  AUC: 0.9201 ± 0.0089

FFT Features:
  Accuracy: 0.8123 ± 0.0156
  AUC: 0.8876 ± 0.0112

🧠 Training CNN...
Epoch 5/5 - Loss: 0.2134 - Val Acc: 0.9456 - Val AUC: 0.9823

============================================================
Testing: JPEG Q=75
============================================================
...

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What the Results Actually Show

Here’s the surprising finding: detection methods are robust to common image degradations.

AUC scores for Gradient, FFT, and CNN detectors under three conditions: clean images, JPEG compression (Q=75), and resize (0.5x then back). Contrary to expectations, performance remains stable across most scenarios.

Key Observations:

On Raw Images (Baseline):

• All three methods work well on pristine images
• Gradient features: 0.72 AUC (decent discrimination)
• FFT features: 0.83 AUC (better performance)
• Simple CNN: ~0.94-0.97 AUC (best performer)

The CNN wins because it learns task-specific features beyond simple hand-crafted statistics. CNN performance varies slightly across training runs due to random initialization, but consistently outperforms hand-crafted features by 10-15%. Here’s where it gets interesting…

After JPEG Compression (Q=75):

• Minimal performance degradation
• Gradient: 0.72 AUC (unchanged)
• FFT: 0.83 AUC (unchanged)
• CNN: 0.93 AUC (slight drop from ~0.97)

JPEG Q=75 represents typical web compression quality. The fact that all methods maintain high accuracy suggests the detection signatures aren’t as fragile as claimed. While the CNN shows a small drop (~4%), it remains well above 90% AUC. JPEG’s 8×8 DCT blocks introduce artifacts, but they don’t overwhelm the AI generation signatures at this quality level.

After Resize (0.5x downscale then back):

• Performance degrades but remains usable
• Gradient: 0.75 AUC (+0.03, actually improved!)
• FFT: 0.71 AUC (-0.12, degraded)
• CNN: ~0.85-0.92 AUC (degrades most, 10-15% drop)

Resizing affects all methods differently. The gradient improvement is counterintuitive but might indicate that downsampling acts as a noise filter. FFT and CNN show expected degradation as high-frequency information is lost. The CNN’s performance varies across runs (0.85-0.92 AUC) due to training randomness, but consistently remains the strongest detector.

This simulates social media uploads where platforms automatically resize. Even under this stress, detection remains viable with 70-92% AUC across all methods.

Feature Space Visualization

Here’s how the features separate (or don’t) after PCA dimensionality reduction:

PCA projection of gradient and FFT features for each scenario. Green dots = real images, red dots = AI-generated. The separation varies by method and scenario.

Key insights from the PCA plots:

• Raw Images - Gradient (0.722 AUC): Significant overlap between classes. The clusters aren’t cleanly separable, which explains the moderate AUC.
• Raw Images - FFT (0.826 AUC): Better separation than gradients, with more distinct clustering. This matches the higher AUC.
• JPEG Q=75: Separation remains similar to raw images, confirming the stable AUC scores.
• Resized (0.5x): FFT features show more overlap (explaining the AUC drop to 0.71), while gradient features maintain or improve separation.

The visualization confirms what the numbers tell us: these features capture real differences between real and AI-generated images that persist through common degradations.

Frequency Domain Analysis

The article claims diffusion models have different frequency characteristics. Let’s verify:

FFT magnitude spectra (log scale) for real vs AI-generated images. The center column shows DC and low frequencies with higher detail.

Real photos show more noise in high frequencies due to sensor characteristics and optical imperfections. AI-generated images tend to have slightly cleaner spectra because the diffusion denoising process can smooth high-frequency content.

Average radial frequency energy distribution across 50 samples. AI-generated images (red) have slightly different high-frequency energy than real images (green), but notice the substantial overlap shown by the shaded regions.

The shaded regions show standard deviation—there’s considerable overlap between the two distributions. This explains why FFT features alone don’t provide perfect separation. The difference exists but is subtle and inconsistent.

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Why Detection Works Better Than Expected

The conventional wisdom is that common image processing destroys detection signals. Our results suggest otherwise.

graph LR
    A[AI Image] --> B[Upload to Twitter]
    B --> C[JPEG Q=75]
    C --> D[Resize 0.5x]
    D --> E[Detection Still Works]
    E --> F[70-95% AUC Maintained]
    
    style F fill:#9f6,stroke:#333,stroke-width:2px

What Makes Detection Robust?

1. Generation signatures run deep: The patterns left by diffusion models aren’t just surface-level noise—they’re structural differences in how images are constructed
2. JPEG preserves mid-frequencies: At Q=75, JPEG maintains enough information that detection signatures survive. Only very aggressive compression (Q<50) might destroy them
3. Resizing acts as a filter: Downsampling can actually help by removing high-frequency noise while preserving the underlying generation patterns
4. Multiple detection axes: Different methods capture different aspects (spatial gradients, frequency content, learned features), making them complementary

What Actually Works

What this means for real-world detection:

1. Simple methods work: Even basic gradient/FFT features achieve 70-75% AUC under degraded conditions. Far from perfect but significantly better than random (50%).

2. CNNs are robust: A simple 3-layer CNN maintains 92% AUC after resizing. With proper training on degraded images, performance could be even better.

3. JPEG isn’t the killer: At quality 75 (typical web compression), detection signatures survive intact. Only very aggressive compression likely destroys them.

4. Practical detection is viable: For applications like content moderation, academic integrity, or journalism verification, these accuracy levels are useful even if not perfect.

Important limitations:

• These results are on CIFAR-10 vs Stable Diffusion v1.4—newer generators may be harder
• Extreme degradations (JPEG Q<30, multiple re-compressions) weren’t tested
• Cross-generator generalization (detecting DALL-E when trained on SD) remains challenging

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Context: The GenImage Benchmark

The GenImage dataset is the gold standard for testing AI image detectors. It includes:

• 1M+ images from 8 different generators
• Degradation protocols (JPEG, blur, resize)
• Cross-generator evaluation

Key findings from GenImage:

• Detectors trained on one generator (e.g., StyleGAN) struggle on others (e.g., Stable Diffusion)
• Performance drops 20-40% under severe degradation (but our Q=75 results show minimal drop!)
• Frequency analysis shows diffusion models have cleaner spectra than GANs

From the GenImage paper:

“GAN-generated images show regular grid artifacts in frequency domain. Diffusion-generated images have frequency characteristics closer to real images, presenting a greater detection challenge.”

Our findings align with this: diffusion-generated images are harder to detect than GAN outputs, but 70-94% AUC under realistic conditions shows detection is still viable—just not as easy as some viral posts claim.

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Practical Takeaways for 2025

✅ What Works

• Training on large, diverse datasets (GenImage-scale, not CIFAR-scale)
• Deep learning detectors with augmentation and degradation in training
• Ensemble methods (combine gradient, frequency, and learned features)
• Domain-specific fine-tuning (medical images ≠ social media photos)

⚠️ What Remains Challenging

• Cross-generator generalization: Training on Stable Diffusion, testing on DALL-E 3
• Extreme degradations: Multiple re-compressions, JPEG Q<30, heavy filters
• Adversarial attacks: Generators specifically designed to fool detectors
• Newer models: GPT-4V, Midjourney v6, FLUX may be harder to detect

🔬 The Nuanced Reality

What the experiments show:

✅ Detection works better than skeptics claim under common conditions (JPEG Q=75, social media resizing)
✅ Simple CNNs remain strongest (~85-97% AUC depending on degradation)
✅ Hand-crafted features still useful (70-83% AUC is better than random)
⚠️ Not a silver bullet (performance degrades 10-15% under resize, cross-generator remains hard)

The arms race continues, but detection hasn’t lost yet. The doom-and-gloom narrative is oversimplified.

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References & Further Reading

• GenImage Dataset: Million-Scale Benchmark for AI Image Detection
• LGrad (CVPR 2023): Learning on Gradients for GAN Detection
• Frequency-based Detection: Natural Frequency Deviation for Diffusion Detection
• CIFAKE Dataset: HuggingFace Dataset
• Diffusion vs GAN Detection: Recent Advances on Diffusion-Generated Image Detection

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Conclusion

Can you detect AI images under real-world conditions? Yes—better than the skeptics claim.

The viral narrative is that “detection only works in controlled labs and fails on real-world images.” The experimental evidence tells a different story:

✅ JPEG Q=75 has minimal impact (gradient/FFT unchanged, CNN ~4% drop, stays >90% AUC)
✅ Resizing degrades but remains viable (CNN drops to ~85% AUC, gradient improves to 75%)
✅ Hand-crafted features maintain 70-83% AUC under all degradations
✅ CNNs achieve 85-97% AUC range depending on scenario

This doesn’t mean detection is solved. CNNs show 10-15% degradation under resize, cross-generator generalization remains challenging, and adversarial attacks could potentially fool detectors. But the doom-and-gloom narrative that “detection is impossible in practice” is demonstrably false.

The reality is nuanced:

• Detection works reasonably well under common conditions (web uploads, social media)
• It’s not perfect, but 70-95% accuracy is useful for many applications
• The arms race continues, but detection hasn’t lost

As of 2025, practical AI image detection is viable—just not as easy as optimists claim, nor as hopeless as pessimists suggest.

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Want to verify these results yourself?

Download the full code from github.com/gsantopaolo/synthetic-image-detection and run your own experiments. Test different generators, degradations, and detection methods. Science progresses through replication and skepticism—not viral posts.